Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations

Sellitto, Pasquale; Gaudel, Audrey; Sauvage, Bastien

doi:https://doi.org/10.5194/egusphere-2024-3748

Preprints

https://doi.org/10.5194/egusphere-2024-3748

Preprints

23 Jan 2025

| 23 Jan 2025

Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations

Pasquale Sellitto, Audrey Gaudel, and Bastien Sauvage

Abstract. We use an offline radiative transfer model driven by IAGOS aircraft observations, to estimate the tropospheric ozone radiative forcing (RF) at decadal time scale (two time intervals between 1994–2004 and 2011–2016 or 2019), over 11 selected Northern Hemispheric regions. On average, we found a systematic positive trend in the tropospheric ozone column (TOC) for both time intervals, even if trends are reduced in 2019 (∆TOC +2.5±1.7 DU, +9.3±7.7 %) with respect to 2011–2016 (∆TOC +3.6±2.0 DU, +14.9±11.5 %). The reduced TOC average trend in 2019 with respect to 2011–2016, originates mostly from decreases of the lower tropospheric ozone column (LTOC) trends and limited variations for upper tropospheric ozone column (UTOC) trends, in the tropics. These average reductions in TOC trends are not accompanied with reductions of the tropospheric ozone RF, between 2011–2016 (4.2±2.4 mW m^-2 per year) and 2019 (3.8±3.6 mW m^-2 per year). This disconnection depends by the smaller RF sensitivity to LTOC than UTOC changes. Correspondingly, the total tropospheric ozone RF sensitivity varies between 18.4±7.4 mW m^-2 per DU, in 2011–2016, and 31.6±20.3 mW m^-2 per DU, in 2019. About 84–85 % of the tropospheric ozone RF occurs in the longwave, with ~4–6 % larger values of this proportion in the tropics than in the extra-tropics. Our estimates are 60–90 % larger than the most recent global average tropospheric ozone RF estimates with online modelling. Our study underlines the importance of the evolution of ozone vertical profiles for the tropospheric ozone RF.

Received: 30 Nov 2024 – Discussion started: 23 Jan 2025

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Journal article(s) based on this preprint

22 Oct 2025

Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations

Pasquale Sellitto, Audrey Gaudel, and Bastien Sauvage

Atmos. Chem. Phys., 25, 13299–13309, https://doi.org/10.5194/acp-25-13299-2025,https://doi.org/10.5194/acp-25-13299-2025, 2025

Short summary

Pasquale Sellitto, Audrey Gaudel, and Bastien Sauvage

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3748', Anonymous Referee #1, 07 Feb 2025
Radiative forcing estimations based on IAGOS mean tropospheric ozone profiles for different regions are provided with offline radiative modelling, enabling to assess the impact of the variability of the vertical distribution of tropospheric ozone on the radiative forcing. The radiative forcing calculations are important and follow a novel approach, that permits to make the distinction between shortwave and longwave RF for the different regions.
General comments
The manuscript deserves publication in ACP, but there is a need for further specifications or more details on the dataset used, some results (obtained values) should be better explained in comparison with other, previous studies, and some sensitivity analyses could be additionally performed.
The manuscript builds further on the Gaudel et al., 2020 (named G20) study, but some more details of interest for the analysis described here should be given: what were the selection criteria for defining the 11 regions (see Table 1)? Are the observations spatially representative for the defined region? Also, in contrast to the G20 study, in which tropospheric ozone trends are calculated based on Quantile Regression on monthly anomalies, tropospheric ozone decadal changes in this manuscript are estimated from the mean tropospheric ozone profiles for different periods, as shown in Figure 2. However, those mean tropospheric ozone profiles might be very dependent on the spatial and temporal distribution of the IAGOS observations over the region or over the time period. For instance, one period might be dominated by summertime observations, while the other period is mainly characterized by wintertime flights. Or, the large majority of the flights might be situated in the beginning of a time period for one region, but at the end of the time period for another region, making the comparison between the regions less meaningful. Also, during the early time period, most profiles might be originating from take-off/landing at the west side of the region, for instance, but on the east side of the region for one of the later periods. On top of that, there is clear temporal sampling difference between the two earlier periods and the year 2019, which will impact the mean tropospheric ozone profiles over the region as well. The impact of possible differences of the spatial and temporal sampling on the different mean tropospheric ozone profiles should, as a consequence, at least be mentioned or even better, somewhat assessed.
Related to this, I would expect to see also the standard deviations of the mean tropospheric ozone profiles included in Fig. 2, in the average LTOC and UTOC in Figure 3, and in the LT, UT, and T ozone percent differences in Figure 4. Only the uncertainties for the worldwide TOC, LTOC and UTOC differences are provided in the text (page 6) and in Table 2, but it is not mentioned how these uncertainties are obtained (statistical mean over the different regions I assume?).
Based on the standard deviations of the mean tropospheric ozone profiles in Fig. 2, one could perform a sensitivity analysis of the RF estimations on the input mean tropospheric ozone profile for each region. Given the comment on how spatial and temporal representative the mean tropospheric ozone profiles for each region are, this RF estimation sensitivity analysis would add an extra feature to your findings.
The obtained (global) RF estimates are compared with previous studies, but not with the values obtained in G20 (Fig. 6) for exactly the same regions, and one of your 2 periods, but with a different method. Why is this comparison not been made? I found this rather strange. It also turns out that your average values are 60 to 90% larger than previous global average estimates with online models, but no explanations for this rather large offset have been given. The authors should go more in depth on this.
As many studies in the TOAR Special Issue pointed out, there was a decrease of tropospheric ozone column amounts during the COVID-19 period (and still continuing today). Have the authors not considered to quantify the impact of this effect on the RF forcing estimations by including a more recent year(s) than 2019 in their analysis? The authors should make reference to this (post-)COVID impact on tropospheric ozone and comment on their choice.
Specific comments
Line 13: add “the year 2019”

Line 45: remove “a” before “tropospheric ozone”

Line 109: have additionally been

Fig. 2: apart from adding standard deviations, show the profiles up to 11 km, as the UT and T ozone columns are defined up to 11 km.

Line 137: “2016” instead of “206”

Lines 156-157: Just to give an example of my previous comment on the spatial or temporal sampling: How confident are you that the higher LTOC increase for Western North America in 2019 (+12.5%) than in 2011-2016 (+3.5%) compared to 1994-2004, is not due to the fact that the 2019 sample is dominantly made up by summertime months, compared to the 2011-2016 sample?

Lines 186-189: Where do I have to note that the uncertainty of the decadal trends is increasing? In Table 3? But these are uncertainties over the regions, right? And also the trends themselves are increasing. Please clarify these statements.

Line 194: 31.7 mW m^-2 instead of W m^-2

Line 196: Try to give an explanation for this finding.
Citation: https://doi.org/10.5194/egusphere-2024-3748-RC1
- AC1: 'Reply on RC1', Pasquale Sellitto, 30 Jul 2025
  
  Dear Reviewer,
  thank you for your comments and suggestions, and your constructive criticism. Please find our point-by-point reply to your review in the attached file.
  Kind regards,
  Pasquale Sellitto on behalf of all coauthors.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3748-AC1
RC2:
'Comment on egusphere-2024-3748', Anonymous Referee #2, 18 Apr 2025
The study by Sellitto et al., titled “Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations” uses aircraft observations and an off-line radiative transfer model (RTM) to investigate decadal ozone radiative forcing (RF). Overall, this is an interesting study within the scope of ACP. However, more details on the methods are required before it can be accepted for publication in ACP.
General Comments:
The IAGOS forms a core part of the analysis in this paper, however, there is no discussion on the quality of the data used. How was the data filtered for anomalous or spurious values/profiles. Secondly, the data is averaged into regions (e.g. Figure 2 vertical profiles) but there is no information on the variability. I think it would be useful to add some information on this. Also, some information on how the regions are defined would be useful (e.g. map with regions shown etc.).

The presentation of Figures 3 and 5 needs to be improved. It is not easy to see the difference between the vertical bars for the different time periods. I would suggest keeping the same colours but maybe changing the colour fill (e.g. hatching or dots etc.) for some of the bars.

When you are calculating the RF, you are using the aircraft profiles and the RTM to calculate the radiative effect (RE) and then taking the different between two time periods (my interpretation anyway). It would be useful if this could be made clearer in the manuscript.

For the time periods used, why only use 2019 for the last year. Would it make more sense to use e.g. 1994-2004, 2008-2012 and 2015-2019? That way you are using multiple years for a time average and investigating changes across a more distributed timeline. However, if you stick with the original time periods/years, the authors need to justify why this is the case. I’m a bit concerned of interpreting the results for just 2019 (i.e. one-year) when the other periods are at least 5-10 years in length.

The authors also say that their results show a RF estimate which is 60-90% larger than e.g. Skeie et al., (2020). However, from my understanding, the Skeie et al., (2020) estimate is based on global model simulations, while the values derived in this study are for the tropics and northern mid-latitudes. Therefore, I think this needs to be clearly stated when the comparisons are made in the manuscript.

When you derive the trend in RF (e.g. RHS column in Table 3), how is this done? The time period e.g. 1994-2004 covers 10-11 years, so when calculating the rate per year, how to do you determine the start and end points. For instance, when comparing 1994-2004 to 2011-2016, would that be e.g. the midpoints 1999-2013 which is 14-years? This needs to be clarified.

Discussion on Table 5 and Equations 1-3 seems a bit rushed to me (only two sentences). I believe there is an opportunity for a more detailed discussion on this. For instance, Rap et al., (2015) and Pope et al., (2024) discuss the sensitivity of the RE wrt tropospheric ozone (i.e. normalised tropospheric ozone radiative effect). How do your calculated values of RE for each time period compare with those values?

To calculate the upper and full tropospheric column of ozone, you fix the tropopause at 11 km. The tropopause will depend heavily on latitude, so this could lead to misleading column values. I know the authors say that a chemical tropopause value of 125 ppbv of ozone is used, so stratospheric air is not influencing the column values, however, the tropopause can reach up to 15-20 km in the tropics, so you might be underestimating the tropical tropospheric column amounts and associated RF.

Minor Comments:
The abstract was a bit confusing. The use of 2011-2016 and 2019 becomes clearer when reading the full paper, but if only looking at the abstract, it is difficult to understand which values are associated with each later time period. Therefore, I suggest rewording this part of the abstract.

“m-2” should be superscript in many places in the abstract.

Page 2 Line 49: Pope et al., (2024) focus on satellite data between 2008 and 2017, not 2019.

Page 3 Lines 85-87 are unclear. Please reword.

Page 5 Line 127: Instead of “aren’t”, I suggest “are not”. This occurs a couple of times in the manuscript.

Page 6 Line 135: The difference value is smaller than the uncertainty (or range). So, do you have confidence that this is an increasing UT O3 value? Secondly, what is the metric in the brackets based on? Is it an uncertainty metric or range in the data?

Page 7 Line 142. Please define VOCs in the first instance earlier in the manuscript.

Figure 4: Would does Figure x refer to?

Figure 5: Suggest making the dotted lines may be dashed lines. They don’t look overly clear to me. Just a suggestion though.

References:
Rap, A., Richard, N. A. D., Forster, P. M., Monks, S. A., Arnold, S. R., and Chipperfield, M. P.: Satellite constraint on the tropospheric ozone radiative effect, Geophys. Res. Lett., 42, 5074– 5081, https://doi.org/10.1002/2015GL064037, 2015.
Pope, R. J., Rap, A., Pimlott, M. A., Barret, B., Le Flochmoen, E., Kerridge, B. J., Siddans, R., Latter, B. G., Ventress, L. J., Boynard, A., Retscher, C., Feng, W., Rigby, R., Dhomse, S. S., Wespes, C., and Chipperfield, M. P.: Quantifying the tropospheric ozone radiative effect and its temporal evolution in the satellite era, Atmos. Chem. Phys., 24, 3613–3626, https://doi.org/10.5194/acp-24-3613-2024, 2024.
Citation: https://doi.org/10.5194/egusphere-2024-3748-RC2
- AC2: 'Reply on RC2', Pasquale Sellitto, 30 Jul 2025
  
  Dear Reviewer,
  thank you for your comments and suggestions, and your constructive criticism. Please find our point-by-point reply to your review in the attached file.
  Kind regards,
  Pasquale Sellitto on behalf of all coauthors.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3748-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3748', Anonymous Referee #1, 07 Feb 2025
Radiative forcing estimations based on IAGOS mean tropospheric ozone profiles for different regions are provided with offline radiative modelling, enabling to assess the impact of the variability of the vertical distribution of tropospheric ozone on the radiative forcing. The radiative forcing calculations are important and follow a novel approach, that permits to make the distinction between shortwave and longwave RF for the different regions.
General comments
The manuscript deserves publication in ACP, but there is a need for further specifications or more details on the dataset used, some results (obtained values) should be better explained in comparison with other, previous studies, and some sensitivity analyses could be additionally performed.
The manuscript builds further on the Gaudel et al., 2020 (named G20) study, but some more details of interest for the analysis described here should be given: what were the selection criteria for defining the 11 regions (see Table 1)? Are the observations spatially representative for the defined region? Also, in contrast to the G20 study, in which tropospheric ozone trends are calculated based on Quantile Regression on monthly anomalies, tropospheric ozone decadal changes in this manuscript are estimated from the mean tropospheric ozone profiles for different periods, as shown in Figure 2. However, those mean tropospheric ozone profiles might be very dependent on the spatial and temporal distribution of the IAGOS observations over the region or over the time period. For instance, one period might be dominated by summertime observations, while the other period is mainly characterized by wintertime flights. Or, the large majority of the flights might be situated in the beginning of a time period for one region, but at the end of the time period for another region, making the comparison between the regions less meaningful. Also, during the early time period, most profiles might be originating from take-off/landing at the west side of the region, for instance, but on the east side of the region for one of the later periods. On top of that, there is clear temporal sampling difference between the two earlier periods and the year 2019, which will impact the mean tropospheric ozone profiles over the region as well. The impact of possible differences of the spatial and temporal sampling on the different mean tropospheric ozone profiles should, as a consequence, at least be mentioned or even better, somewhat assessed.
Related to this, I would expect to see also the standard deviations of the mean tropospheric ozone profiles included in Fig. 2, in the average LTOC and UTOC in Figure 3, and in the LT, UT, and T ozone percent differences in Figure 4. Only the uncertainties for the worldwide TOC, LTOC and UTOC differences are provided in the text (page 6) and in Table 2, but it is not mentioned how these uncertainties are obtained (statistical mean over the different regions I assume?).
Based on the standard deviations of the mean tropospheric ozone profiles in Fig. 2, one could perform a sensitivity analysis of the RF estimations on the input mean tropospheric ozone profile for each region. Given the comment on how spatial and temporal representative the mean tropospheric ozone profiles for each region are, this RF estimation sensitivity analysis would add an extra feature to your findings.
The obtained (global) RF estimates are compared with previous studies, but not with the values obtained in G20 (Fig. 6) for exactly the same regions, and one of your 2 periods, but with a different method. Why is this comparison not been made? I found this rather strange. It also turns out that your average values are 60 to 90% larger than previous global average estimates with online models, but no explanations for this rather large offset have been given. The authors should go more in depth on this.
As many studies in the TOAR Special Issue pointed out, there was a decrease of tropospheric ozone column amounts during the COVID-19 period (and still continuing today). Have the authors not considered to quantify the impact of this effect on the RF forcing estimations by including a more recent year(s) than 2019 in their analysis? The authors should make reference to this (post-)COVID impact on tropospheric ozone and comment on their choice.
Specific comments
Line 13: add “the year 2019”

Line 45: remove “a” before “tropospheric ozone”

Line 109: have additionally been

Fig. 2: apart from adding standard deviations, show the profiles up to 11 km, as the UT and T ozone columns are defined up to 11 km.

Line 137: “2016” instead of “206”

Lines 156-157: Just to give an example of my previous comment on the spatial or temporal sampling: How confident are you that the higher LTOC increase for Western North America in 2019 (+12.5%) than in 2011-2016 (+3.5%) compared to 1994-2004, is not due to the fact that the 2019 sample is dominantly made up by summertime months, compared to the 2011-2016 sample?

Lines 186-189: Where do I have to note that the uncertainty of the decadal trends is increasing? In Table 3? But these are uncertainties over the regions, right? And also the trends themselves are increasing. Please clarify these statements.

Line 194: 31.7 mW m^-2 instead of W m^-2

Line 196: Try to give an explanation for this finding.
Citation: https://doi.org/10.5194/egusphere-2024-3748-RC1
- AC1: 'Reply on RC1', Pasquale Sellitto, 30 Jul 2025
  
  Dear Reviewer,
  thank you for your comments and suggestions, and your constructive criticism. Please find our point-by-point reply to your review in the attached file.
  Kind regards,
  Pasquale Sellitto on behalf of all coauthors.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3748-AC1
RC2:
'Comment on egusphere-2024-3748', Anonymous Referee #2, 18 Apr 2025
The study by Sellitto et al., titled “Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations” uses aircraft observations and an off-line radiative transfer model (RTM) to investigate decadal ozone radiative forcing (RF). Overall, this is an interesting study within the scope of ACP. However, more details on the methods are required before it can be accepted for publication in ACP.
General Comments:
The IAGOS forms a core part of the analysis in this paper, however, there is no discussion on the quality of the data used. How was the data filtered for anomalous or spurious values/profiles. Secondly, the data is averaged into regions (e.g. Figure 2 vertical profiles) but there is no information on the variability. I think it would be useful to add some information on this. Also, some information on how the regions are defined would be useful (e.g. map with regions shown etc.).

The presentation of Figures 3 and 5 needs to be improved. It is not easy to see the difference between the vertical bars for the different time periods. I would suggest keeping the same colours but maybe changing the colour fill (e.g. hatching or dots etc.) for some of the bars.

When you are calculating the RF, you are using the aircraft profiles and the RTM to calculate the radiative effect (RE) and then taking the different between two time periods (my interpretation anyway). It would be useful if this could be made clearer in the manuscript.

For the time periods used, why only use 2019 for the last year. Would it make more sense to use e.g. 1994-2004, 2008-2012 and 2015-2019? That way you are using multiple years for a time average and investigating changes across a more distributed timeline. However, if you stick with the original time periods/years, the authors need to justify why this is the case. I’m a bit concerned of interpreting the results for just 2019 (i.e. one-year) when the other periods are at least 5-10 years in length.

The authors also say that their results show a RF estimate which is 60-90% larger than e.g. Skeie et al., (2020). However, from my understanding, the Skeie et al., (2020) estimate is based on global model simulations, while the values derived in this study are for the tropics and northern mid-latitudes. Therefore, I think this needs to be clearly stated when the comparisons are made in the manuscript.

When you derive the trend in RF (e.g. RHS column in Table 3), how is this done? The time period e.g. 1994-2004 covers 10-11 years, so when calculating the rate per year, how to do you determine the start and end points. For instance, when comparing 1994-2004 to 2011-2016, would that be e.g. the midpoints 1999-2013 which is 14-years? This needs to be clarified.

Discussion on Table 5 and Equations 1-3 seems a bit rushed to me (only two sentences). I believe there is an opportunity for a more detailed discussion on this. For instance, Rap et al., (2015) and Pope et al., (2024) discuss the sensitivity of the RE wrt tropospheric ozone (i.e. normalised tropospheric ozone radiative effect). How do your calculated values of RE for each time period compare with those values?

To calculate the upper and full tropospheric column of ozone, you fix the tropopause at 11 km. The tropopause will depend heavily on latitude, so this could lead to misleading column values. I know the authors say that a chemical tropopause value of 125 ppbv of ozone is used, so stratospheric air is not influencing the column values, however, the tropopause can reach up to 15-20 km in the tropics, so you might be underestimating the tropical tropospheric column amounts and associated RF.

Minor Comments:
The abstract was a bit confusing. The use of 2011-2016 and 2019 becomes clearer when reading the full paper, but if only looking at the abstract, it is difficult to understand which values are associated with each later time period. Therefore, I suggest rewording this part of the abstract.

“m-2” should be superscript in many places in the abstract.

Page 2 Line 49: Pope et al., (2024) focus on satellite data between 2008 and 2017, not 2019.

Page 3 Lines 85-87 are unclear. Please reword.

Page 5 Line 127: Instead of “aren’t”, I suggest “are not”. This occurs a couple of times in the manuscript.

Page 6 Line 135: The difference value is smaller than the uncertainty (or range). So, do you have confidence that this is an increasing UT O3 value? Secondly, what is the metric in the brackets based on? Is it an uncertainty metric or range in the data?

Page 7 Line 142. Please define VOCs in the first instance earlier in the manuscript.

Figure 4: Would does Figure x refer to?

Figure 5: Suggest making the dotted lines may be dashed lines. They don’t look overly clear to me. Just a suggestion though.

References:
Rap, A., Richard, N. A. D., Forster, P. M., Monks, S. A., Arnold, S. R., and Chipperfield, M. P.: Satellite constraint on the tropospheric ozone radiative effect, Geophys. Res. Lett., 42, 5074– 5081, https://doi.org/10.1002/2015GL064037, 2015.
Pope, R. J., Rap, A., Pimlott, M. A., Barret, B., Le Flochmoen, E., Kerridge, B. J., Siddans, R., Latter, B. G., Ventress, L. J., Boynard, A., Retscher, C., Feng, W., Rigby, R., Dhomse, S. S., Wespes, C., and Chipperfield, M. P.: Quantifying the tropospheric ozone radiative effect and its temporal evolution in the satellite era, Atmos. Chem. Phys., 24, 3613–3626, https://doi.org/10.5194/acp-24-3613-2024, 2024.
Citation: https://doi.org/10.5194/egusphere-2024-3748-RC2
- AC2: 'Reply on RC2', Pasquale Sellitto, 30 Jul 2025
  
  Dear Reviewer,
  thank you for your comments and suggestions, and your constructive criticism. Please find our point-by-point reply to your review in the attached file.
  Kind regards,
  Pasquale Sellitto on behalf of all coauthors.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3748-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Pasquale Sellitto on behalf of the Authors (30 Jul 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (31 Jul 2025) by Andreas Petzold

RR by Roeland Van Malderen (15 Aug 2025)

Suggestions for revision or reasons for rejection

Dear authors,

Thanks for taking carefully into account my comments and doing the additional analyses with an extended time interval (2017-2019) and the (post—COVID time interval (2000-2023). I agree with you that these analyses greatly improved the manuscript. When my comments and suggestions have not been taken into account, the authors provide meaningful arguments not to do so, so I believe the manuscript is ready for publication in ACP. I mainly have some minor or technical comments, mostly related to the (visibility of the) figures.

* Line 39: change to “was found to continue increasing well”
* Line 46: change to “ground-based, ozonesonde and satellite observations”
* Fig. 2: as already asked in my previous review report: why not extending the profiles up to 11 km (instead of 10 km)? You define the TOC and UTOC up to 11 km, so it would make sense to show the average profiles also up to 11 km!
* Fig 2: I found the very hard to distinguish between the mean vertical profiles colored the grey and black on one hand, and red and dark red line on the other hand (in my printed version of the manuscript). Why not using 4 different colors here? Or increasing the contrast between grey and black and red and dark red and using dotted lines for two of them?
* Lines 145-146: “the general reduction” of UTOC in 2020-2023 seems to contrast with the positive values between the brackets (+0.5+/-1.1 DU, +6.6+/-13.9%  also add DU here). So, please specify the reference period for the reduction (I guess w.r.t. 2017-2019?) here!
* Fig. 3: here again, if you would change the colors in Fig. 2, this would also increase the readability between the different column bars: the difference between red and dark red is very hard to distinquish!
* Fig. 5: the difference between the two column bars of the different regions would be clearer (especially if they are close) if they would have an outside border line. The horizontal dotted lines that represent the uncertainty range of the solid lines are very hard to read (in my printed version). Couldn’t you work e.g. with an uncertainty interval to the right of the figure (the horizontal solid lines are extended to the right and its uncertainty is marked there?).
* Line 263: Replace with “Three time intervals”

Hide

ED: Publish subject to minor revisions (review by editor) (03 Sep 2025) by Andreas Petzold

Dear Pasquale,

my apologies for the slow response but I was waiting for feedback from one of the reviewers on the revised version.
Since I still didn't get response, I reviewed the revised paper by myself and follow the suggestion of Reviewer 1.

You received feedback from reviewer 1 which I repeat here:

Thanks for taking carefully into account my comments and doing the additional analyses with an extended time interval (2017-2019) and the (post—COVID time interval (2000-2023). I agree with you that these analyses greatly improved the manuscript. When my comments and suggestions have not been taken into account, the authors provide meaningful arguments not to do so, so I believe the manuscript is ready for publication in ACP. I mainly have some minor or technical comments, mostly related to the (visibility of the) figures.

* Line 39: change to “was found to continue increasing well”
* Line 46: change to “ground-based, ozonesonde and satellite observations”
* Fig. 2: as already asked in my previous review report: why not extending the profiles up to 11 km (instead of 10 km)? You define the TOC and UTOC up to 11 km, so it would make sense to show the average profiles also up to 11 km!
* Fig 2: I found the very hard to distinguish between the mean vertical profiles colored the grey and black on one hand, and red and dark red line on the other hand (in my printed version of the manuscript). Why not using 4 different colors here? Or increasing the contrast between grey and black and red and dark red and using dotted lines for two of them?
* Lines 145-146: “the general reduction” of UTOC in 2020-2023 seems to contrast with the positive values between the brackets (+0.5+/-1.1 DU, +6.6+/-13.9%  also add DU here). So, please specify the reference period for the reduction (I guess w.r.t. 2017-2019?) here!
* Fig. 3: here again, if you would change the colors in Fig. 2, this would also increase the readability between the different column bars: the difference between red and dark red is very hard to distinquish!
* Fig. 5: the difference between the two column bars of the different regions would be clearer (especially if they are close) if they would have an outside border line. The horizontal dotted lines that represent the uncertainty range of the solid lines are very hard to read (in my printed version). Couldn’t you work e.g. with an uncertainty interval to the right of the figure (the horizontal solid lines are extended to the right and its uncertainty is marked there?).
* Line 263: Replace with “Three time intervals”

Please implement the minor changes. After receipt of the second revision I will quickly proceed with the final decision.
Thank you for your patience.

Best wishes
Andreas

Hide

AR by Pasquale Sellitto on behalf of the Authors (09 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (12 Sep 2025) by Andreas Petzold

AR by Pasquale Sellitto on behalf of the Authors (13 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (17 Sep 2025) by Andreas Petzold

AR by Pasquale Sellitto on behalf of the Authors (17 Sep 2025)

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Pasquale Sellitto on behalf of the Authors (20 Oct 2025) Author's adjustment Manuscript

EA: Adjustments approved (20 Oct 2025) by Andreas Petzold

Journal article(s) based on this preprint

22 Oct 2025

Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations

Pasquale Sellitto, Audrey Gaudel, and Bastien Sauvage

Atmos. Chem. Phys., 25, 13299–13309, https://doi.org/10.5194/acp-25-13299-2025,https://doi.org/10.5194/acp-25-13299-2025, 2025

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Pasquale Sellitto, Audrey Gaudel, and Bastien Sauvage

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Cumulative views and downloads (calculated since 23 Jan 2025)

Month	HTML	PDF	XML	Total
Jan 2025	126	22	14	162
Feb 2025	66	8	6	80
Mar 2025	21	3	1	25
Apr 2025	32	6	3	41
May 2025	22	4	1	27
Jun 2025	29	3	3	35
Jul 2025	24	9	5	38
Aug 2025	104	7	1	112
Sep 2025	388	5	1	394
Oct 2025	14	5	1	20

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Tropospheric ozone is a potent greenhouse gas; its anthropogenic levels rise contributes to climate change. We evaluate tropospheric ozone trends and climate impacts with aircraft data and a radiative model, comparing the baseline period (1994–2004) to 2011–2016 and 2019. Tropospheric ozone levels increased significantly but with a smaller trend in 2019 than in 2011–2016. However, ozone radiative forcing did not decrease between these periods because of different vertical distribution evolutions


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