The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across
society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns,
and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and
data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it
is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining
the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence
and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic
CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and
manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are
given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot
spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of
cou...

Global anthropogenic CO 2 emissions and uncertainties as a prior for Earth system modelling and data assimilation

10.5194/essd-13-5311-2021