| 18 Nov 2025
Global CO emissions and drivers of atmospheric CO trends constrained by MOPITT satellite observations
Abstract. Carbon monoxide (CO), an important atmospheric pollutant produced from incomplete combustion and hydrocarbon oxidation, significantly influences atmospheric chemistry and air quality. Accurate quantification of its global emissions and the underlying drivers of atmospheric trends is essential for understanding and improving global environmental conditions. Using 20 years (2003–2022) of satellite observations from the Measurement of Pollution in the Troposphere (MOPITT) instrument, here we analyze changes in global CO emissions and atmospheric concentrations. The a posteriori simulations show improved consistency with independent surface and aircraft measurements compared to the a priori simulations. Sensitivity analyses further confirm that inferred emissions remain robust against uncertainties associated with satellite vertical sensitivity and variations in hydroxyl radical (OH) concentrations. Our results indicate a substantial decline in global anthropogenic CO emissions of 14–17 % (approximately 85–110 Tg) over the two-decade period, largely driven by reductions in the United States, Europe, and eastern China. In contrast, biomass burning emissions exhibited strong interannual variability, with recent increases in Northern Hemisphere high-latitude forests. A key finding is that rising biomass burning emissions have offset about 37 % of the global anthropogenic emission reduction (47 % in the Northern Hemisphere alone), underscoring the considerable moderating influence of wildfires on atmospheric composition trends. This study provides a comprehensive assessment of global CO emissions and the mechanisms governing atmospheric CO trends, offering a scientific basis for integrated policies addressing both climate change and air pollution.
The manuscript «Global CO emissions and drivers of atmospheric CO trends constrained by MOPITT satellite observations» use 20 years of retrieved CO from MOPITT and GEOS-Chem simulations in an 4D-Var assimilation framework to provide inverse estimates of CO emissions. A central finding in this study is a significant long-term decline in global anthropogenic CO emissions, with regional variations. The sensitivity to varying and constant OH as well as using MOPITT CO column or profile data are tested, and the results were not so sensitive to these different choices.
It is very important and useful to evaluate trend in emissions of components important for air-quality and the oxidation capacity of the atmosphere. As the emission inventories to be used in CMIP7 is available (CEDS version 2025, https://github.com/JGCRI/CEDS/ ) it would be very useful if you can compare your results with these. It would also be very useful if you could compare your results with the version of CEDS used in CMIP6 as well. As these emission datasets are commonly used in model simulations, and you use a combination of different emission inventories as your prior, the results will be even more relevant for atmospheric modellers.
As you wrote at the beginning of the introduction: “Carbon monoxide (CO) is a key atmospheric pollutant produced from incomplete combustion and the oxidation of hydrocarbons.» It is no clear for me how you treat the source from oxidation of hydrocarbons in your setup. You wrote: «The CO source from CH4 oxidation is optimized separately as an aggregated global source, with the a priori uncertainty of 25%.» CH4 has increased by ~133 ppb over the period of investigation. Do you take the increase in methane into account? And what about the oxidation of other VOCs? You wrote that you employ a CO only simulation (on Line 108). How is the atmospheric production treated in these simulations? Biogenic VOC are mentioned, but what about anthropogenic VOC emissions? Is the production of CO from biogenic VOCs sensitive to the different OH fields. Only CO loss is mentioned related to the OH sensitivity.
You mention that the biomass burning exhibit strong interannual variability, but indicate with only a single number without any uncertainty on the offset of the increase in the anthropogenic emissions. Uncertainties in these numbers should be added. The biomass burning CO emissions have a large interannual variability, and clearly 2021 will impact your trend results at high latitudes. Would it be interesting to look at how the seasonal emissions and impact on concentration have changed over the 20 year period?
Especially related to biomass burning emissions and corresponding concentration changes but also in general: How are the trends in the manuscript calculated, what are the uncertainties in these trends and are they significant?
Smaller comments:
Abstract: You should mention the model and method used. On L29 you mention posteriori simulations without mentioning the model and method.
Throughout the manuscript: I would not use the word “observations” when the MOPITT CO data is mentioned.
L68: “This has profound implications, as CO shares common combustion sources with major greenhouse gases like methane (CH4) and carbon dioxide”. I did not fully got what you mean here. CO is emitted due to incomplete combustions. CO2 is also emitted from complete combustion of fossil fuels. We should reduce emissions of greenhouse gases CO2 and CH4 and as a co-benefit CO emissions are reduced. Another co-benefit is less global warming and reduced climate induced biomass burning CO emissions.
L87 and L106: HEMCO is not defined.
L113: For the reader it would be good if you can say if there are any trend in OH in that dataset. Looking at Fig. S2, what is the unit on the y-axis?
L115: Not all emission inventories used cover all years up to 2023. What is the end year and what do you do with the following years?
L155: HIPPO not defined yet. ATOM and WDCGG not defined yet either.
L157: “accumulation of biases over preceding months”. Can these biases influence your trend results?
L198: “low cloud observations” unclear what it means. Clouds at low altitudes?
L269: What is the difference between the results in this study and Fortems-Cheiney?
L291: Sentence starting with “Comparisons with the CEDS inventory..” This is more a description of the anthropogenic emissions in CEDS rather than a comparison with the CEDS inventory. As mentioned above, a comparison with CEDS (both CMIP6 version and CMIP7 version will be useful).
L299: What is an environmental Kuznets curve?
L303: Would also be useful to compare with other emission inventories, as GFED5 which is recently released. https://www.globalfiredata.org/data.html
L459: Series of sensitivity experiments. I can not see that these sensitivity experiments to attribute concentration changes to different drivers are defined.
Table 2: What is the unit of the trend. I find it easier to read the table if the unit is added to the table. Eg. after Emissions and Trend. What is the definition of the trend uncertainty estimate.
Table 3: The trend is missing in this table. Important to also add the uncertainty in the trend calculations here.
Fig. 1: Is Fig1c CO emissions? CO production due to photooxidation of biogenic VOCs? As mentioned above, what about anthropogenic VOCs? And methane? The colour scale in a to c should be the same for easier comparison.
Fig. 3: Add y-label. This comment is relevant for several figures. Here it would be very useful if the CEDS emissions to be used in the upcoming CMIP7 as well as those emissions used in CMIP6 (up to 2014) could be added.
Fig. 4: Maybe due to the limited description of how the attribution to the different sources were made, why did you exclude areas with large biomass burning emissions from the trends in anthropogenic and biogenic VOCs?
Fig. 5: Here you both show the large interannual variability of the biomass burning emissions and the seasonal variability. One of the main point you made is the offset of the air quality improvement due to reduction in anthropogenic CO emissions by the increase in biomass burning CO emissions. Clearly the year 2021 will impact the results. How robust is the trend you calculate. Would it be more interesting to look at trend in different seasons and the impact on air quality for seasons. At high latitudes, vegetations do not burn during winter.
Fig. 7: “Only stations with 20 year observations (the time range between the first and last observations) during 2003-2022 are included” Can you simply say, “Only stations with observations from 2003 to 2022 are included”?