the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Aug 2026
Tropospheric Oxidation Chemistry and Methane Growth: A Process-Based Attribution of OH Variability (2003–2022)
Abstract. Earth system models that include interactive chemistry provide a mechanistic understanding of the hydroxyl radical (OH) sources and sinks. However, their use is often limited in the estimation of the global methane (CH4) budget. We conduct 20-year emission-driven methane simulations (2003–2022) with the Community Earth System Model Version 2.2 (CESM2.2) nudged to different meteorological datasets and input various chemical emissions, including the Seventh Coupled Model Intercomparison Project (CMIP7). Our evaluation includes in-situ observations from the NOAA Marine Boundary Layer Reference and airborne field studies, as well as ground-based and satellite remote sensing observations of carbon monoxide (CO) and CH4. We find that the model configurations with a lower OH bias have improved skill in reproducing both CO and CH4 spatial and temporal variations, including the CH4 annual growth rate. We quantify the OH impacts on the CH4 growth rate and derive a comprehensive attribution of OH changes to Earth System chemical processes. Despite an interannual variability lower than 4 %, changes in OH explains around 30 % of the interannual variability of the CH4 growth throughout the 2003–2022 period. The OH changes arise from both internal variability —via biogenic and lightning emission responses— and prescribed surface emissions, with remaining uncertainties. We find that OH is well buffered against chemistry perturbations, as increased chemical complexity raises the OH recycling probability. Consequently, studies relying on simplified and prescribed OH representations may neglect these complex chemical feedbacks and overestimate the role of OH changes in driving the CH4 growth rate.
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Status: open (until 25 Sep 2026)
- RC1: 'Comment on egusphere-2026-4883', Anonymous Referee #1, 04 Sep 2026 reply
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- 1
In Gaubert et al., the authors use multiple simulations of the CESM model, with different meteorology and emissions, to understand relationships between OH and the methane growth rate, as well as the drivers of interannual variability in OH. The paper is suitable for publication in ACP once the below comments are addressed:
General Comments:
The paper could be strengthened greatly by re-writing for clarity. The enumerated main conclusions in the final section are important and definitely supported by the work, but I’m not sure I would have realized those were the major points you were trying to emphasize. I would consider reducing some unnecessary text (there are many paragraphs that are simply repeating numbers in tables and figures) and reorganizing some paragraphs (e.g., the paragraph starting in line 418 is about 1.5 pages in length) to try to improve clarity. There are also, not infrequently, grammatical errors that need to be corrected
Your focus is primarily on globally averaged statistics, which for CH4 is appropriate, but for CO, which is much shorter lived, and the OH budget, which can vary substantially in the horizontal and vertical, some discussion of their spatial heterogeneity is warranted. How does the relative importance of the different OH budget terms vary spatially? Does looking at your results from a more regional perspective change the results in Figures 10 & 8 significantly?
Specific Comments:
Figure 1 caption: I assume the text at the end starting with “At the bottom right,…” is from a previous version of the figure. If not, your meaning here is unclear and the text should be changed.
Line 163: should be “emissions from vegetation are estimated…” not “is estimated”.
Figure 2, panel g: Some discussion about the cause of the dramatic difference in lightning NOx between the ERA and MERRA2 datasets starting in 2020 is warranted, particularly given results you discuss in Section 6. Do you know what caused the sudden drop in the ERA emissions? Do you have any indication which is likely to be correct?
Section 2.2: It would also be worthwhile to try and evaluate the accuracy of your modeled OH, since you talk about the OH budget later on and any extrapolations about the impacts of OH on CO and CH4 in the actual atmosphere depend on your modeled OH accuracy. While obviously a more difficult comparison given the diurnal variability in OH, at the very least, showing that your modeled spatial and temporal variability agree with in situ observations ( e.g. from ATom or KORUS-AQ) or inversion derived trends within reason would further strengthen your argument.
Figure 4: I'm not sure the coloring of your boxes is particularly helpful. I found it somewhat confusing. Maybe you could just consider, putting in bold, values above/below a certain threshold?
Section 3.2: Is there any spatial or temporal variability in these statistics? Numbers averaged globally can hide all sorts of issues, particularly with something like bias where you can have areas of large negative and positive bias that cancel each other out. Adding a map or two would be helpful (even as a supplementary figure).
Line 330: “Despite different OH…”. It would be useful to have maps showing the climatological mean OH for your different simulations so readers can understand the spatial variability in OH in the different models. The spatial variability, while potentially not very important for CH4 given its long lifetime, would be important for CO, which has a much shorter lifetime.
Line 418: Sentence starting with “Tropospheric OH generally follows…” needs a citation.
Line 427: “…, confirming that interannual variability is controlled almost entirely by OH chemistry”. Do you mean IAV in the loss of CH4 or in the CH4 growth rate?
Line 469: Does this methodology take into account cross-correlations between the different terms, for example surface NOx and anthropogenic CO emissions? If not, how will that affect your conclusions, as I don’t think it would be appropriate to add the various contributions since they aren’t independent.