the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 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.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5432', Anonymous Referee #1, 10 Dec 2025
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RC2: 'Comment on egusphere-2025-5432', Anonymous Referee #2, 02 Jan 2026
Overall Comment:
This paper is relevant, well written and helps to explore recent trends in CO concentrations, which have not been clearly understood previously. It has potentially important impacts for the link between carbon monoxide and climate-change (and wildfires) and I endorse the publication of this manuscript, provided the suggestions below are addressed.
My primary concern is the structure of the manuscript. I would suggest moving Section 3.3 to allow a more cohesive paper and have explained this in more detail in the specific comments below. A clearer model description (and explanation of the tagged-CO mode) is also required to explore how the VOCs and OH are being treated by the model and a budget comparison to other studies would benefit the paper. Some minor changes to the figures are suggested in the final section of comments, mostly relating to consistency between the plots. More generally, there are assumptions made throughout the manuscript which are not clearly explained and instances where comparisons are made between model runs or observations and model runs where it is not clearly defined which way the comparison is being done, and so the difference becomes difficult to interpret. These need to be addressed before publication.
Specific Comments:
Line 24: suggest changing atmospheric chemistry to atmospheric oxidation capacity
Line 26: clarify ‘global environmental conditions’.
Line 28: remove ‘here’.
Line 29 - 30: Change ‘the a posteriori’ and ‘the a priori’ to ‘a posteriori’ and ‘a priori’.
Line 35-36: ‘In contrast’ is confusing as the opposite to the decreasing trend in anthropogenic emissions is an increasing trend, not interannual variability, which is the next statement made. In Line 305 it is stated that there is no significant long-term trend in the biomass burning emissions. Make this clear in the abstract as it currently reads as though there may be an increasing trend.
Line 37: In the conclusion, 47% is discussed, not 37%. I think this is perhaps the CO column concentration, but 37% is not mentioned again. Please clarify.
Line 55: Include further information on the impacts of CO on air quality and climate.
Line 65: Explain further ‘growing sensitivity to climate change’.
Line 92: In particular.
Line 107: What is the model top altitude?
Line 108: More detail needed to explain the tagged-CO mode.
Line 123: Comment on the trends in CO budget and compare to other budgets e.g. Zheng et al. 2019 (https://essd.copernicus.org/articles/11/1411/2019/). This could be included as text or a table, and if possible include sink sizes also.
Line 134: What is the 50% assumed a priori error based on?
Line 134: Consider renaming as the GC-original simulation also uses a priori emissions inventories, so naming the simulation with the Kalman filter as a priori is confusing. Suggest name change to GC-Kalman.
Line 157: Clarify how model spin-up period was decided.
Line 159: (and S3 caption). What are the red-grids referring to? The figure shows a pink mask over the oceans to my eye, not red grids.
Line 175: Include a description of how OH (and VOCs) is being modelled before the experiments are explained. I think it would also add clarity to the paper to instead rename these experiments with Profile and Column not Col and Prof as shortening by only a few letters seems unnecessary and will aid the flow of the text.
Line 198: Justify the exclusion of data below this threshold.
Line 199: Is there any danger of a systematic bias being introduced by only using day-time data? For example, anthropogenic emissions likely having a diurnal cycle. Clarify if comparisons to observational data have been corrected, or if effort has been taken to compare to day-time observations.
Line 223: Smaller not lower.
Line 228: Not clear why the units have swapped in these lines from the above units.
Line 246: This section would benefit with a description as to how source types are discerned in the model (e.g. how emissions are determined to be anthropogenic or biomass in origin). This may be placed earlier with the expanded definition of the tagged-CO mode.
Line 248: 7-14% higher when? Is this per year, or over the whole period? Clarify which experiment is higher (only the a priori is mentioned).
Line 251: Global anthropogenic emissions?Line 273: China or East China?
Line 274: The growth period until 2007 is only clear in the GC-original line. Consider including a growth rate value to clarify.
Line 300: India’s growth rate does not appear to be significant in recent years. Include figures to demonstrate this point.
Line 316: Add time period to the statement regarding a dramatic increase.
Line 319: What do the two regions discussed contribute to global biomass burning emissions in other years (i.e. is 2021 unique?)
Line 322: Has global burned area decreased globally or just in these regions discussed?
Line 330: Explain how climate variability impacts the CO emissions in this case.
Line 341: Figure 4 has total emissions not combustion emissions. I think this also includes the NMVOC related sources of CO and so Figs 4j-l should not be discussed simply as combustion.
Line 349: And industrialization in some regions.
Line 361: This section would benefit from some organizing/ structure. For example, a full description of the anthropogenic emissions and then biomass burning and then global rather than swapping between the two continuously.
Line 407: Explain the compensation by CO emissions. Can it be determined what the VOC source of CO is doing in this comparison? Is there any compensation through this route?
Line 422: Add more detail about the optimized boundary conditions (sometimes they are discussed as land boundary and sometimes as ocean boundary). Is 50% a good approximation for both the Var and Fix OH experiments? Consider the use of this section if the results are so limited. It may be better placed as supplementary information or in the methods section rather than results.
Line 441: This section may be better placed before section 3.3. Alternatively, as suggested above, section 3.3 may be better placed with the supplement or methods section as the assumptions described mean results are difficult to interpret (e.g. due to the optimized land condition). I think section 3.3 is interesting and important to include, but the manuscript would benefit from a more coherent structure.
Line 459: Describe the sensitivity experiments, perhaps in the methods section.
Line 460: Reference Figure 9a).
Line 486: % sign missing.
Line 490: This is not concomitant as higher biomass burning emissions are not naturally associated with the lower anthropogenic emissions.
Line 510: Be clear about the direction of comparison for the bias (observations to model or model to observations). In these comparisons is this the column total difference? Below there is reference to the surface concentrations.
Line 516: Is this total reduction in emissions (or burden?) or should this be annual?
Line 520: Make clear any links between biomass burning emissions and the difference in climate over the period of the study.
Figures and Tables:
1: Colourbar is misleading, how can it go below zero? Suggestion to start the white at zero for a) b) c).
4: Include the exclusion of certain grid-cells in the text and give a reason for this.
6: Suggestion to not include data for the oceans if it is being ignored in the analysis. This data could be masked out.
7: Move titles to above the plots.
8 and 9: These colour schemes look different to those used in previous figures. Ensure this is consistent and also perhaps one which has more differentiation in the negative end of the bar would be beneficial as the blues are quite hard to distinguish.
S1: Include a key for the colours used in e) and d)
S4: Fix the titles and the detail on this figure is difficult to pick out. Suggest trying larger marker points or a more differential colour scheme to make the differences clearer.
Citation: https://doi.org/10.5194/egusphere-2025-5432-RC2
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- 1
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”?