(Towards) anthropogenic CO2
emissions through inverse
modelling
Frédéric Chevallier
LSCE, France
The PIs of the TCCON,
as listed on https://tccon-wiki.caltech.edu/
(Towards) anthropogenic CO2
emissions through inverse
modelling
Frédéric Chevallier
LSCE, France
The PIs of the TCCON,
as listed on https://tccon-wiki.caltech.edu/
Outline


On the maturity of CO2 inverse modeling
Development of CO2 observation systems about the
anthropogenic emissions
CO2 observations

Surface air-sample measurements

Retrievals of CO2 partial column
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

Retrievals of CO2 total column
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

Satellites: AIRS, IASI, TES, …
[not demonstrated with real data]
Satellites: SCIAMACHY, GOSAT
Surface: TCCON
Aircrafts
Atmospheric inverse modeling

Infer carbon surface fluxes from their measured impact on carbon
concentrations
CO2 surface fluxes
Observations
Atmospheric inverse modeling

Infer carbon surface fluxes from their measured impact on carbon
concentrations
 Technology borrowed from Numerical Weather Prediction data
assimilation systems
CO2 surface fluxes
Observations
Atmospheric inversions

Pros

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Directly provides uncertainty estimates
Exhaustive, effective and not-intrusive approach
NRT possible
Refines prior inventories – ultimate estimation
Cons

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Heavy, sophisticated numerical technology
Potentially expensive network deployment
Shall involve prior information
Involves chemistry-transport models
Sectorization not straightforward
Are inversion statistics reliable?



In its most rigorous form, the inversion problem is expressed by
Bayes’ theorem
The output of an inversion is not a deterministic field but a
multidimensional probability density function
How reliable are the second order moments of the pdf (variances
and covariances)?
TCCON inversion vs. air-sample
inversion

Aggregation of weekly/3.75ox2.5o results at the annual subcontinental scale
WDCGG, NOAA,
RAMCES, CarboEurope
databases
Chevallier et al., submitted to GRL
Evaluating inversion error statistics
at local scale


Use air-sample inversion
Compare with y = TCCON measurements at 14 stations
 E[ ( Hxb – y ) ( Hxb – y )T ] = HBHT+R
 E[ ( Hxa – y ) ( Hxa – y )T ] = HAHT+R
Zooming


CO2 inversion is a mature field, even though all scientific
questions have not been answered
Increase the resolution
 of the inverse systems
 of the observation systems
Mesoscale inverse modelling

Europe at 50km resolution using the CHIMERE model
 Summer 2006
 15 CO2 stations of the CarboEurope-Integrated Project
 Comparison with spatial averages of gap-filled CarboEurope
flux measurements L4 product
 Broquet et al. 2011
Obs.
Inversion
Prior
Towards the inversion of the
anthropogenic component (1/2)

Development of regional networks in Paris, Los Angeles,
Indianapolis, …
Indianapolis-Flux
Davis et al., 2010
CO2-MegaParis
Towards the inversion of the
anthropogenic component (2/2)


Development of regional networks in Paris, Los Angeles,
Indianapolis, …
Strategies
 Add expert knowledge: prescribe some of the space-time
pattern of the anthropogenic emissions and adjust a few
degrees of freedom only
 Aggregation errors to be taken into account
 Add measurements: tracers of anthropogenic emissions (CO,
NOx, 14CO2, …) together with CO2
 Model errors (chemistry) to be taken into account
Imaging the anthropogenic plumes
from space (1/2)

CarbonSat is a satellite project which is being developed by ESA,
as a candidate for the Earth Explorer Opportunity Mission to be
launched in 2018 earliest
 Measures the atmospheric concentrations of CO2 and CH4 with
high spatial resolution (2 x 2 km2) and good spatial coverage
(500 km swath width)
 Focus on hot-spot sources, like power plants
 Could be operated as a constellation
Imaging the anthropogenic plumes
from space (2/2)

Accurate wind knowledge is key to the success of the mission
Towards anthropogenic CO2 emissions
through inverse modelling

What EO/GMES techniques have been used to verify emissions
inventories?
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What key data is needed for this verification?
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Depends on observation density - can be properly estimated
What future developments/enhancements are possible?
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Dense observations around the target regions, of CO2 and of tracers of
anthropogenic activity
What are the levels of uncertainty in datasets?


CO2 inverse modelling systems are being developed for this purpose
The main developments expected are on the observation side
Can EO/GMES techniques replace traditional emissions inventory
estimates (which use statistical data)?

Inverse modelling refines prior inventories but do not replace them
Thank you for your attention