the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 May 2023
Benefits of Net Zero policies for future ozone pollution in China
Oliver Wild
Ruth M. Doherty
Fiona M. O’Connor
Steven T. Turnock
Abstract. Net Zero emission policies principally target climate change, but may have a profound influence on surface ozone pollution. To investigate this, we use a chemistry-climate model to simulate surface ozone changes in China under a Net Zero pathway, and examine the different drivers that govern these changes. We find large monthly mean surface ozone decreases of up to 16 ppb in summer and small ozone decreases of 1 ppb in winter. Local emissions are shown to have the largest influence on future ozone changes, outweighing the effects of changes in emissions outside China, changes in global methane concentrations and a warmer climate. Impacts of local and external emissions show strong seasonality, with the largest contributions to surface ozone in summer, while changes in global methane concentrations have a more uniform effect throughout the year. We find that while a warmer climate has a minor impact on ozone change compared to the Net Zero scenario, it will alter the spatial patterns of ozone in China, leading to ozone increases in the south and ozone decreases in the north. We also apply a deep learning model to correct biases in our ozone simulations, and to provide a more robust assessment of ozone changes. We find that emission controls may lead to a surface ozone decrease of 5 ppb in summer. This is smaller than that simulated with the chemistry-climate model, reflecting overestimated ozone formation under present-day conditions. Nevertheless, this assessment clearly shows that the strict emission policies needed to reach Net Zero will have a major benefit in reducing surface ozone pollution and the occurrence of high ozone episodes, particularly in high-emission regions in China.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(7000 KB)
-
Supplement
(5500 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(7000 KB) - Metadata XML
-
Supplement
(5500 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Zhenze Liu et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-230', Anonymous Referee #2, 08 Jun 2023
The authors quantified the future ozone improvement from the Net Zero policies by using the chemistry-climate model and deep learning model, and examined the different drivers of such changes in designed scenarios. I think this paper is interesting, and this topic of great importance to policy makers. However, several major revisions should be addressed before the recommendation.
First, unlike the PM2.5 pollution, ozone pollution is heavily affected by the meteorological variations from historical experiences (e.g., the period of 2013-2020), especially in the context of future warming climate. However, I didn’t see the clear descriptions of meteorological data used for this study in the Method part, and how it changes over times and its effect and the interaction with climate change on future ozone pollution.
Second, is there any limitation or uncertainty by further applying the deep learning model (i.e., the correction ratio of historical results) to correct the biases in surface O3 simulated with UKESM1 in future analysis?
Third, the authors should reorganize the result part, I cannot understand that why the “Bias corrected surface O3 under the Net Zero pathway” part is presented at last in the results part as it is the basement of ozone improvement and drivers analysis, I think.
Fourth, several limitations and uncertainties indeed exist in this study, for example, the model itself and meteorological data used, which should be systematically presented at the end of the manuscript.
Minor comments:
- Add quantitative results in the Abstract part.
- Add more introduction of current ozone pollution condition in the first paragraph.
- Please check and revise all the maps with 9-dash-lines in China.
- Please explain the effect of external emissions outside China (like a circle) in Figure 2b (probably wrong, please check).
- Part 3: more quantitative results (i.e., numbers) should be added.
- Figure 3, again, the small effect of climate on ozone pollution seems weird for me. Please add more explanation.
- As I know, generally model simulation has relatively bad performances on high ozone concentration (i.e., summer), while from Figures 6c and 6f, the model seems be not good in March, April (not the period of high values), please explain the reason.
- There are many recent studies analyzing future ozone pollution in the context of carbon neutrality, please add the comparison.
Citation: https://doi.org/10.5194/egusphere-2023-230-RC1 -
RC2: 'Comment on egusphere-2023-230', Anonymous Referee #1, 19 Jul 2023
This manuscript simulated surface ozone changes in China under a Net Zero pathway using a chemistry-climate model which have corrected biases through deep learning model. The results indicated a substantial decrease in the monthly average surface ozone concentrations in the summer, with the greatest contribution from local emission reductions. The entire study appears technically sound, and the results are well interpreted. Thus, I recommend the publication by addressing the comments below.
- For line 125, In addition to ozone, are there any other variables included as input in the deep learning model?
- Why does methane contribute more significantly to ozone increase in western China compared to eastern China?
- For figure 7d, the authors suggest that the significant smaller decreases of latitudinal mean surface O3 implies underlying impacts of emission controls on O3 may not be as large as the model suggests, and the overestimation of O3 responses to emission changes. However, by comparing figure 7 (a) and figure 1 (a), it can be observed that the current smaller decrease between the two scenarios after correcting with deep learning model is primarily due to a reduction in the corrected ozone concentrations. Does this imply that the decrease of ozone cannot fully demonstrate the reduction in pollution emissions?
Citation: https://doi.org/10.5194/egusphere-2023-230-RC2 -
AC1: 'Response to reviewers comments', Zhenze Liu, 10 Aug 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-230/egusphere-2023-230-AC1-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-230', Anonymous Referee #2, 08 Jun 2023
The authors quantified the future ozone improvement from the Net Zero policies by using the chemistry-climate model and deep learning model, and examined the different drivers of such changes in designed scenarios. I think this paper is interesting, and this topic of great importance to policy makers. However, several major revisions should be addressed before the recommendation.
First, unlike the PM2.5 pollution, ozone pollution is heavily affected by the meteorological variations from historical experiences (e.g., the period of 2013-2020), especially in the context of future warming climate. However, I didn’t see the clear descriptions of meteorological data used for this study in the Method part, and how it changes over times and its effect and the interaction with climate change on future ozone pollution.
Second, is there any limitation or uncertainty by further applying the deep learning model (i.e., the correction ratio of historical results) to correct the biases in surface O3 simulated with UKESM1 in future analysis?
Third, the authors should reorganize the result part, I cannot understand that why the “Bias corrected surface O3 under the Net Zero pathway” part is presented at last in the results part as it is the basement of ozone improvement and drivers analysis, I think.
Fourth, several limitations and uncertainties indeed exist in this study, for example, the model itself and meteorological data used, which should be systematically presented at the end of the manuscript.
Minor comments:
- Add quantitative results in the Abstract part.
- Add more introduction of current ozone pollution condition in the first paragraph.
- Please check and revise all the maps with 9-dash-lines in China.
- Please explain the effect of external emissions outside China (like a circle) in Figure 2b (probably wrong, please check).
- Part 3: more quantitative results (i.e., numbers) should be added.
- Figure 3, again, the small effect of climate on ozone pollution seems weird for me. Please add more explanation.
- As I know, generally model simulation has relatively bad performances on high ozone concentration (i.e., summer), while from Figures 6c and 6f, the model seems be not good in March, April (not the period of high values), please explain the reason.
- There are many recent studies analyzing future ozone pollution in the context of carbon neutrality, please add the comparison.
Citation: https://doi.org/10.5194/egusphere-2023-230-RC1 -
RC2: 'Comment on egusphere-2023-230', Anonymous Referee #1, 19 Jul 2023
This manuscript simulated surface ozone changes in China under a Net Zero pathway using a chemistry-climate model which have corrected biases through deep learning model. The results indicated a substantial decrease in the monthly average surface ozone concentrations in the summer, with the greatest contribution from local emission reductions. The entire study appears technically sound, and the results are well interpreted. Thus, I recommend the publication by addressing the comments below.
- For line 125, In addition to ozone, are there any other variables included as input in the deep learning model?
- Why does methane contribute more significantly to ozone increase in western China compared to eastern China?
- For figure 7d, the authors suggest that the significant smaller decreases of latitudinal mean surface O3 implies underlying impacts of emission controls on O3 may not be as large as the model suggests, and the overestimation of O3 responses to emission changes. However, by comparing figure 7 (a) and figure 1 (a), it can be observed that the current smaller decrease between the two scenarios after correcting with deep learning model is primarily due to a reduction in the corrected ozone concentrations. Does this imply that the decrease of ozone cannot fully demonstrate the reduction in pollution emissions?
Citation: https://doi.org/10.5194/egusphere-2023-230-RC2 -
AC1: 'Response to reviewers comments', Zhenze Liu, 10 Aug 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-230/egusphere-2023-230-AC1-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
Zhenze Liu et al.
Zhenze Liu et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 410 | 156 | 17 | 583 | 44 | 12 | 18 |
- HTML: 410
- PDF: 156
- XML: 17
- Total: 583
- Supplement: 44
- BibTeX: 12
- EndNote: 18
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(7000 KB) - Metadata XML
-
Supplement
(5500 KB) - BibTeX
- EndNote
- Final revised paper