the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploring the Drivers of Tropospheric Hydroxyl Radical Trends in the GFDL AM4.1 Atmospheric Chemistry-Climate Model
- 1Princeton University, Program in Atmospheric and Oceanic Science
- 2NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
- 1Princeton University, Program in Atmospheric and Oceanic Science
- 2NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
Abstract. We explore the sensitivity of modelled tropospheric hydroxyl (OH) concentration trends to meteorology and near-term climate forcers (NTCFs), namely methane (CH4); nitrogen oxides (NOx = NO2 + NO); carbon monoxide (CO); non-methane volatile organic compounds (NMVOCs); and ozone-depleting substances (ODS) using the Geophysical Fluid Dynamics Laboratory’s (GFDL) atmospheric chemistry-climate model, Atmospheric Model version 4.1 (AM4.1) driven by emissions inventories developed for the Sixth Coupled Model Intercomparison Project (CMIP6) and forced by observed sea surface temperatures and sea ice prepared in support of the CMIP6 Atmospheric Model Intercomparison Project (AMIP) simulations. We find that the modelled tropospheric airmass-weighted mean [OH] has increased by ~5 % globally from 1980 to 2014. We find that NOx emissions and CH4 concentrations dominate the modelled global trend, while CO emissions and meteorology were also important in driving regional trends. Modelled tropospheric NO2 column trends are largely consistent with those retrieved from the Ozone Monitoring Instrument (OMI) satellite, but simulated CO column trends generally overestimate those retrieved from the Measurements of Pollution in The Troposphere (MOPITT) satellite, possibly reflecting biases in input anthropogenic emission inventories, especially over China and South Asia.
Glen Chua et al.
Status: open (until 20 Feb 2023)
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RC1: 'Comment on acp-2023-9', Anonymous Referee #1, 28 Jan 2023
reply
This manuscript presents an examination of hydroxyl radical trends, variability, and sensitivity from the GFDL model AM4.1 for 1980-2014. In addition to a “Base” run and a “Met” run in which all emissions are fixed to 1980 levels, sensitivity simulations are also performed in which emissions for individual species (NOx, CH4, CO, NMVOCs, and ODSs) are fixed to 1980 to isolate spatial and temporal effects on OH abundance. Results suggest that global mean OH concentration has increased by ~5%, mainly due to the competing effects of increasing NOx and CH4. Model validation against OMI NO2 and MOPITT CO is performed, revealing that NO2 compares reasonably well while modeled CO trends compare poorly against observations (which reflects more on the emissions inventory than on the model).
Overall, I consider this to be a nice analysis that makes a solid contribution to the literature surrounding OH concentrations at the global scale. Sensitivity simulations like the ones performed here are valuable for gleaning information about the drivers of OH variability, with interesting, if perhaps somewhat expected, conclusions found in the spatial and temporal details of the various analyses of the simulations. I consider the comparison to observations to be sufficient for this study – there are always additional datasets that can be compared against, but for the species examined and the motivation of this work, the two included make sense. Prior literature is well cited, and the present work is well contextualized with comparisons to the results of other studies. The article is well within the scope of ACP, and, after addressing a number of comments included below, I would consider it a good candidate for publication.
Specific comments:
Table 1: Curious that a wavelength cutoff of 310 nm is used for O3->O1D photolysis; most other models (e.g., Lelieveld et al., 2016, which you compare to throughout this manuscript) use 330 nm due to the small contribution from the quantum yield tail – see, e.g., Armerdling et al., 1995: https://pubs.acs.org/doi/pdf/10.1021/j100010a025. Any idea, or ability to quantify, how much this might affect total primary production in your results?
L225 / Fig. 2b: While the increase in [OH] in the lower troposphere is largest in absolute terms, [OH] values drop in the UT just as a result of pressure. Would be informative to also see this plot in units of pptv.
L235: I’m curious if the authors see any issue with treating CH4 as a surface boundary condition and making conclusions like “CH4 caused a negative trend in [OH]”. Especially since it’s a problem of “the chicken and the egg” and feedbacks between OH and CH4 are notably missing at the surface, isn’t causation particularly difficult to attribute in this case? Since models are generally not set up to do CH4 fluxes, the model configuration here is understandable; perhaps just worth a note of caution in the text.
Figures 11 and 12: For both panels (b), does this indicate a non-zero emissions trend over the oceans? I don’t see why a trend in emissions for either CO or NO2 should occur, besides for shipping lanes perhaps, but I would expect from the color bar that a zero trend should be depicted as white.
Technical corrections:
L34: “tropospheric” misspelled
L86: “increasing” should be “increase”
L167: Check punctuation; period should be comma.
L203: should be “as well as”
L227: sensitivity misspelled
L243: “the” or “this” should be removed
Figure 2: in panel d), I think the purple bar lost part of its label (should be CH4+NOx? I only see “+NOx”)
L296: should be “out of”
Figure 6: Text on panels c and d should be increased in size
L340: “increases” misspelled
L362: “flux” here is a bit hard to decipher, please clarify
L386: “trends” at beginning of line should be removed; is “Iand” a typo
Figure 11 caption: same as above. Also, I’d suggest avoiding repetition between figure captions and text.
L445: “agrees” should be “agree”
L469: Should this be “SLCF” instead? Defined?
L479: should be “(Horowitz et al., 2020)” all in parentheses?
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RC2: 'Comment on acp-2023-9', Anonymous Referee #2, 30 Jan 2023
reply
Chua et al use the GFDL CCM to understand the modeled trend in tropospheric OH over 1980 -2014 and, through using various sensitivity runs, to tease out the relative importance of the different OH drivers on these trends. Ultimately, they find that the increasing trend in NOX emissions over the period, along with the increase in CH4 abundance, have the largest impact on global OH trends. CO and meteorology, through it’s impact on water vapor abundance, primarily impact interannual variability, although they can affect trends regionally. They also compare modeled trends in CO and NO2 to observed trends from MOPITT and OMI, respectively, finding that the model replicates NO2 trends well but fails to capture CO accurately. They attribute errors in the CO trends to errors in the emissions. Overall, this is a well-written paper that incrementally advances our understanding of OH variability. It is suitable for publication in ACP once the minor revisions below are addressed.
Line 125: Since lightning NOX is so important for OH production, a few more details about how lightning NOX is calculated in the model should be included.
Line 127: Are the surface concentrations of CH4 and the other species set by latitude? What dataset do you use to constrain the values?
Line 130: Should be “A summary of historical emissions … is shown in Fig 1.”
Line 166: You say that you don’t need to evaluate the CH4 since surface values are set as a boundary condition, but this does not necessarily translate to CH4 being correct aloft or even at the surface, since I’m assuming you’re using latitude bands to set the surface concentration. Since CH4 plays such an important role in your results, seeming to be second only in importance to anthropogenic NOX on a global scale, some discussion of how errors in CH4 could affect your results is warranted.
Line 198: Why aren’t you using the most recent OMI NO2 retrieval (v4.0) (Lamsal et al, 2021)? Changes in the air mass factors for the new retrieval have led to some large changes in the retrievals, particularly over highly polluted regions (see Fig. 10 in Lamsal, for example). Are these changes irrelevant for the trends you are studying?
Line 220: Is He et al (2020) using the same simulation you discuss here, or one similar enough in configuration that the OH trends can be compared? Also, in the citations, you list the version of He et al (2020) from ACPD. That should be updated to the finalized version.
Line 300: The dip in 1992 is also evident in the met run, indicating that, for this case, CO isn’t necessarily the main/driving factor. Assuming your simulation includes the effects of the Pinatubo eruption on the stratosphere, isn’t this a more likely explanation for that particular dip, at least in part? There’s no need to get into a discussion about this but maybe just removing the reference to 1992 would simplify things.
Figures 4 and 6: For all panels in Figure 4 and for panels c and d of Figure 6, most of the text is illegible. Please increase the font size.
Line 340: Should be “increases” not “increses”.
Line 385 – 386: Should say “significant positive trends”. Also, I think Iand is supposed to be India?
Section 4.1: Since your model results suggest that CO affects global OH more through IAV than through trends, I think it also warrants some discussion on how well the model captures the CO IAV as compared to MOPITT. Otherwise, I think the MOPITT evaluation section is sufficient in highlighting the potential limitations of the impact of the modeled CO on this analysis.
Line 444: Should be “The increasing CO trends … lead to higher CO levels.”
Figure 13: Something seems off about the methane lifetime for the “Met” run. If I’m understanding correctly, for that simulation, all anthropogenic emissions were held to 1980 values, so while it’s understandable that there would be large differences by the end of the simulation, it seems unrealistic that, in 1981, the CH4 lifetime would differ by more than 1.5 years from the baseline simulation.
Sources:
Lamsal, L. N., Krotkov, N. A., Vasilkov, A., Marchenko, S., Qin, W., Yang, E. S., Fasnacht, Z., Joiner, J., Choi, S., Haffner, D., Swartz, W. H., Fisher, B., and Bucsela, E.: Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments, Atmos. Meas. Tech., 14, 455-479, 10.5194/amt-14-455-2021, 2021.
Glen Chua et al.
Glen Chua et al.
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