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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 “true” fluxes
on European, country, annual and monthly scales. Posterior
monthly and country-aggregated fluxes improved their correlation
coefficient with the “known truth” by 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.
