technical note: atmospheric co2 inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation
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2018
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Abstract
Atmospheric inversions are widely used in the optimization of
surface carbon fluxes on a regional scale using information from
atmospheric CO2 dry mole fractions. In many studies the
prior flux uncertainty applied to the inversion schemes does not
directly reflect the true flux uncertainties but is used to
regularize the inverse problem. Here, we aim to implement an
inversion scheme using the Jena inversion system and applying
a prior flux error structure derived from a model–data residual
analysis using high spatial and temporal resolution over a full year
period in the European domain. We analyzed the performance of the
inversion system with a synthetic experiment, in which the flux
constraint is derived following the same residual analysis but
applied to the model–model mismatch. The synthetic study showed
a quite good agreement between posterior and
truefluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the
known truthby 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.
| Reference Key |
kountouris2018atmospherictechnical
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| Authors | ;P. Kountouris;C. Gerbig;C. Rödenbeck;U. Karstens;U. Karstens;T. F. Koch;M. Heimann |
| Journal | Journal of agricultural and food chemistry |
| Year | 2018 |
| DOI |
10.5194/acp-18-3027-2018
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| URL | |
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