| 27 Mar 2026
CAMS-REG-UNC-v8.1: A detailed uncertainty product for the gridded CAMS-REG-v8.1 emission inventory
Abstract. Independent verification of greenhouse gas emission reductions and trends in air pollution levels is receiving increasing attention. Atmospheric observations can provide such a constraint on emission estimates through inverse modelling, which requires a detailed quantification of uncertainties in prior emission inventories, observations and chemical transport models. This paper describes a detailed methodology to quantify uncertainties in a state-of-the-art European emission inventory: CAMS-REG. Uncertainties are estimated for all input data used to create the emission inventory and propagated to the final product. This results in separate uncertainty estimates for country-level emissions per sector and uncertainties in the spatial allocation of those emissions. Ideally, the gridded emission uncertainties should add up to the country-level emission uncertainties and for this purpose an optimization procedure was developed (only for countries with detailed emission reporting). This results in (scaled) gridded emission uncertainties and spatial error correlation lengths, which are included in the final dataset. The gridded uncertainty maps show large differences between pollutants and countries, representing the variability in input data and their reliability. CO2 shows the smallest gridded (optimized) uncertainties, with a median relative standard deviation of 15 % (interquartile range: 9 % – 25 %). The largest gridded (optimized) uncertainties are found for NMVOC: 45 % (38 % – 58 %). This work follows up from previous efforts and details the first comprehensive emission uncertainty dataset for Europe. The data are available from Zenodo: https://doi.org/10.5281/zenodo.18400810 (Super et al., 2026).
This paper presents a methodology to quantify uncertainties in the CAMS-REG European emission inventory, providing both country-level and gridded uncertainty estimates for greenhouse gases and air pollutants to support inverse modelling and emission verification.
The paper is interesting, well written, and cover a much needed aspect of emission inventories preparation.
However, I have some points I would like to see covered and improved before possible publication
- Table 1: why is this table only available for CO2 and CH4? Could you extend this to cover also air pollutants?
- Table 2: please better explain how these values are derived
- section 2.3. I re-read few times this section, but I have to say it's quite complex, and I would like to see this improved, for better clarity. I.e., could you describe the steps in a workflow/graph? There are some efforts in this direction (as Figure 2) but this section 2.3 should be improved, as it is the core of the paper, and at the moment is quite complex to follow
- similar to section 2.3, also section 2.4 should be improved, for better clarity
- Figure 2: also I found this Figure complex, and I cannot easily link it to the rest of the text...could you please improve its readability?
- Table 3 shows optimized and non-optimized uncertainties. To my knowledge, the optimized uncertainties values you get are quite low...are these values strongly affected by the uncertainties values you use as input, and if yes, did you implement a proper sensitivity analysis? Also, as these optimized values are quite small...are there other uncertainty source that should be considered and are not considered in your modelling, to fully define emission uncertainties? please specify
- Section 5 (data availability)... I checked the provided data...I think, to facilitate their use, on top of the usual CAMS-REG format for the data, one should provide a gridded uncertainty data (or at least a python code to convert the CAMS-REG format to a more simple gridded uncertainties field).