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. Quantification of greenhouse gas emissions is receiving alot of attention because of its relevance for climate mitigation.Complementary to official reported bottom-up emission inventories,quantification can be done with an inverse modelling framework, combiningatmospheric transport models, prior gridded emission inventories and anetwork of atmospheric observations to optimize the emission inventories. Animportant aspect of such a method is a correct quantification of theuncertainties in all aspects of the modelling framework. The uncertaintiesin gridded emission inventories are, however, not systematically analysed.In this work, a statistically coherent method is used to quantify theuncertainties in a high-resolution gridded emission inventory of CO2and CO for Europe. We perform a range of Monte Carlo simulations todetermine the effect of uncertainties in different inventory components,including the spatial and temporal distribution, on the uncertainty in totalemissions and the resulting atmospheric mixing ratios. We find that theuncertainties in the total emissions for the selected domain are 1 % forCO2 and 6 % for CO. Introducing spatial disaggregation causes asignificant increase in the uncertainty of up to 40 % for CO2and 70 % for CO for specific grid cells. Using gridded uncertainties, specificregions can be defined that have the largest uncertainty in emissions andare thus an interesting target for inverse modellers. However, the largestsectors are usually the best-constrained ones (low relative uncertainty), sothe absolute uncertainty is the best indicator for this. With this knowledge,areas can be identified that are most sensitive to the largest emissionuncertainties, which supports network design.

atmospheric chemistry and physics

Uncertainty analysis of a European high-resolution emission inventory of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and CO to support inverse modelling and network design