Projected future changes in extreme precipitation over China under stratospheric aerosol intervention

Wang, Ou; Liang, Ju; Gu, Yuchen; Haywood, Jim M.; Chen, Ying; Fang, Chenwei; Wang, Qingeng

doi:https://doi.org/10.5194/egusphere-2023-2904

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https://doi.org/10.5194/egusphere-2023-2904

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09 Jan 2024

| 09 Jan 2024

Projected future changes in extreme precipitation over China under stratospheric aerosol intervention

Ou Wang, Ju Liang, Yuchen Gu, Jim M. Haywood, Ying Chen, Chenwei Fang, and Qingeng Wang

Abstract. Extreme precipitation events are linked to severe economic losses and casualties in China every year; hence, exploring the potential mitigation strategies to minimize these events and their changes in frequency and intensity under global warming is of importance, particularly for the populous subregions. In addition to global warming scenarios, this study examines the effects of the potential deployment of stratospheric aerosol injection (SAI) on hydrological extremes in China based on the SAI simulations (G6sulfur) of the Geoengineering Model Intercomparison Project (GeoMIP) from UKESM1 (The UK Earth System Model) simulations. The simulated SAI deployment is compared with simulations of the future climate under two different emission scenarios (SSP5-8.5 and SSP2-4.5) and reduction in the solar constant (G6solar) to understand the effect of SAI on extreme precipitation patterns. The results show that, under future global warming scenarios, precipitation and extreme wet climate events are projected to increase by 2100 relative to the present day across all the subregions in China. Additionally, analyses of extreme drought events show a projected increase in southern China. The G6sulfur and G6solar experiments ameliorate the increases in extreme rainfall intensities, especially for the eastern subregions of China. The impacts of SAI in decreasing extreme precipitation events and in consecutive wet days are more pronounced than in G6solar. While the results from both G6sulfur and G6solar show encouraging abatement of many of the impacts on detrimental extreme events that are evident in SSP5-8.5 there are some exceptions. Both G6sulfur and G6solar show drying trendsat high latitudes within the region, which is consistent with our understanding of the spin-down of the hydrological cycle under SRM. For instance, the projected dry days increase for G6sulfur compared to SSP5-8.5. These side effects imply that a cautionary approach and further optimization may be required should any future SRM deployment be considered.

Received: 04 Dec 2023 – Discussion started: 09 Jan 2024

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Journal article(s) based on this preprint

08 Nov 2024

Projected future changes in extreme precipitation over China under stratospheric aerosol intervention in the UKESM1 climate model

Ou Wang, Ju Liang, Yuchen Gu, Jim M. Haywood, Ying Chen, Chenwei Fang, and Qin'geng Wang

Atmos. Chem. Phys., 24, 12355–12373, https://doi.org/10.5194/acp-24-12355-2024,https://doi.org/10.5194/acp-24-12355-2024, 2024

Short summary

Ou Wang, Ju Liang, Yuchen Gu, Jim M. Haywood, Ying Chen, Chenwei Fang, and Qingeng Wang

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-2904', Anonymous Referee #1, 01 Feb 2024

Please see attached file for comments and suggestions.

Citation: https://doi.org/10.5194/egusphere-2023-2904-RC1
- AC1: 'Reply on RC1', Qin’geng Wang, 02 Apr 2024
  
  Dear Referee,
  Thanks for your comments. Please see the attachment for point-by-point responses.
  Best regards
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC1
- AC2: 'Reply on RC1', Qin’geng Wang, 02 Apr 2024
  
  Publisher’s note: this comment is a copy of AC1 and its content was therefore removed.
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC2
RC2:
'Comment on egusphere-2023-2904', Anonymous Referee #2, 05 Feb 2024

This study uses the UK Earth System Model (UKESM1) simulation results to examine the effect of solar radiation modification (SRM) geoengineering on precipitation extremes in China. As part of the GeoMIP project, UKESM1 was used to conduct two sets of SRM simulations: stratospheric aerosol injection (G6sulfur) and solar constant reduction (G6solar). Both G6sulfur and G6solar simulations are designed in such a way that global mean surface temperature under the scenario of SSP5-8.5 was brought down to the level under SSP2-4.5. Using a set of precipitation extreme indices, the authors investigated the effect of G6sulfur and G6solar on precipitation extremes for different regions of China. The authors found that compared to SSP5-8.5, both G6sulfur and G6solar ameliorate precipitation extremes over different parts of China, but increase drought risks in some northern part of China. The authors also compared the similarities and differences between precipitation extreme response to G6sulfur and G6solar for different regions of China. The analysis of this paper itself is largely sound, but I do not recommend its publication in ACP in the present form for the following reasons:
I see little science in this study. I have to say that I have not carefully examined the Results part, which is just the description of figures with little scientific insight. What the authors did is just to compare simulated precipitation extremes over different regions of China under SS5-8.5, SSP2-4.5, G6solar, and G6sulfur. Regional climate extremes are strongly dependent on the SRM scenarios (location, timing, and intensity of SAI and solar reduction). Also, regional climate extremes are strongly dependent on climate models. If one uses another climate model and/or another SAI strategy, most results presented in this paper might be different. At least, the authors should use multiple model results from GeoMIP instead of just one model. Also, the authors should try to investigate some science underlying the presented precipitation extreme comparisons. For example, why the difference between G6sulfur and G6solar? In the present form, this paper just presents simulation results from a specific model with little interpretations. At least for me, I see little science here.
Some specific comments
Lines 36-59: This first paragraph of the Introduction part is very lengthy and most part is not directly relevant to the study here. For example, the detailed description of extreme precipitation in Zhengzhou and Beijing is not needed at all.
Lines 61-72: This paragraph can also be substantially shortened and combined with the first paragraph.
Line 85: check the grammar here. ‘,the climate’
Lines 90-101: The use of ‘prediction’ in this paragraph is not appropriate.
Lines 98-99: Whether SAI would decrease precipitation depends on the scenario of SAI deployment. Also, instead of Pinto et al. 2020 and Liu and et al. 2021, more influential papers on the climate effect of SAI should be cited.
Line 120: Why only use results from a single model? Why not use multi-model results from GeoMIP?

Lines 186-187: I don’t quite understand this sentence.
Line 199: The word of ‘accurate’ is not appropriate here.
Line 133: ‘reducing the solar constantor increasing SAI’. Check grammar and spelling here.
Lines 225-226: I don’t understand what ‘SAI is sensitive to global warming’ means.
Line 245: Where are ‘the other three G6 models’?

Citation: https://doi.org/10.5194/egusphere-2023-2904-RC2
- AC3: 'Reply on RC2', Qin’geng Wang, 02 Apr 2024
  
  Dear Referee,
  Thanks for your comments. Please see the attachment for point-by-point responses.
  Best regards
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC3
RC3:
'Comment on egusphere-2023-2904', Anonymous Referee #3, 13 Feb 2024

I think this could be a useful study with some work. I always like creative ways of integrating measurements and models. The analysis is also carefully done and focuses on clearly important issues (extremes).
While I don’t dispute any of the findings, my biggest issue is with the explanations. There is a lot of reporting of the results but not much interpretation other than (sometimes) speculating about mechanisms. Given that the authors have a great deal of climate model output at their disposal, they could look into some of these mechanisms. I would point out specific examples, but this seems to be a general issue in Section 3.
Also, there is a lot of discussion of different indices, but they mostly show the same thing. That’s not a problem, but the way you’re describing them makes it seem like you’re going through a laundry list of indices. I’d like to see more insight. Digging into the results in Table 2 would be interesting. For example, _why_ does CWD not behave like the other indices? What’s special about those two regions that have the opposite sign?
Figure 1 and Section 2.1: Any reason you don’t include the Tibetan plateau?
Lines 169-182: This seems like a long way of saying that you used survival functions, which are a perfectly reasonable thing to use for what you want to do.
Lines 186-187: This is not consistent with my understanding of what field significance does. I would appreciate more description as to what you mean.
Figure 2: Can you add a panel showing the bias as a percent instead of an absolute value?
Lines 225-226: I’m not sure what this means. SAI is sensitive to global warming?
Line 265: Typo (depicting)
Line 293: Aggregated how?
Line 295: Be more specific about “the opposite”. Also, what are 0 values in the table? (I can figure it out, but you need a description.)
Lines 324-325: It’s difficult to put these numbers in context. Is 100 mm a lot for these regions?
Line 341: I don’t know if “effectively mitigates” is the correct phrasing. Be more specific.
Lines 391ff: I’ll be honest, I had a hard time with this entire paragraph. I’m really not sure I understand it.
Line 520: ETCCDI

Citation: https://doi.org/10.5194/egusphere-2023-2904-RC3
- AC4: 'Reply on RC3', Qin’geng Wang, 02 Apr 2024
  
  Dear Referee,
  Thanks for your comments. Please see the attachment for point-by-point responses.
  Best regards
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC4

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-2904', Anonymous Referee #1, 01 Feb 2024

Please see attached file for comments and suggestions.

Citation: https://doi.org/10.5194/egusphere-2023-2904-RC1
- AC1: 'Reply on RC1', Qin’geng Wang, 02 Apr 2024
  
  Dear Referee,
  Thanks for your comments. Please see the attachment for point-by-point responses.
  Best regards
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC1
- AC2: 'Reply on RC1', Qin’geng Wang, 02 Apr 2024
  
  Publisher’s note: this comment is a copy of AC1 and its content was therefore removed.
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC2
RC2:
'Comment on egusphere-2023-2904', Anonymous Referee #2, 05 Feb 2024

This study uses the UK Earth System Model (UKESM1) simulation results to examine the effect of solar radiation modification (SRM) geoengineering on precipitation extremes in China. As part of the GeoMIP project, UKESM1 was used to conduct two sets of SRM simulations: stratospheric aerosol injection (G6sulfur) and solar constant reduction (G6solar). Both G6sulfur and G6solar simulations are designed in such a way that global mean surface temperature under the scenario of SSP5-8.5 was brought down to the level under SSP2-4.5. Using a set of precipitation extreme indices, the authors investigated the effect of G6sulfur and G6solar on precipitation extremes for different regions of China. The authors found that compared to SSP5-8.5, both G6sulfur and G6solar ameliorate precipitation extremes over different parts of China, but increase drought risks in some northern part of China. The authors also compared the similarities and differences between precipitation extreme response to G6sulfur and G6solar for different regions of China. The analysis of this paper itself is largely sound, but I do not recommend its publication in ACP in the present form for the following reasons:
I see little science in this study. I have to say that I have not carefully examined the Results part, which is just the description of figures with little scientific insight. What the authors did is just to compare simulated precipitation extremes over different regions of China under SS5-8.5, SSP2-4.5, G6solar, and G6sulfur. Regional climate extremes are strongly dependent on the SRM scenarios (location, timing, and intensity of SAI and solar reduction). Also, regional climate extremes are strongly dependent on climate models. If one uses another climate model and/or another SAI strategy, most results presented in this paper might be different. At least, the authors should use multiple model results from GeoMIP instead of just one model. Also, the authors should try to investigate some science underlying the presented precipitation extreme comparisons. For example, why the difference between G6sulfur and G6solar? In the present form, this paper just presents simulation results from a specific model with little interpretations. At least for me, I see little science here.
Some specific comments
Lines 36-59: This first paragraph of the Introduction part is very lengthy and most part is not directly relevant to the study here. For example, the detailed description of extreme precipitation in Zhengzhou and Beijing is not needed at all.
Lines 61-72: This paragraph can also be substantially shortened and combined with the first paragraph.
Line 85: check the grammar here. ‘,the climate’
Lines 90-101: The use of ‘prediction’ in this paragraph is not appropriate.
Lines 98-99: Whether SAI would decrease precipitation depends on the scenario of SAI deployment. Also, instead of Pinto et al. 2020 and Liu and et al. 2021, more influential papers on the climate effect of SAI should be cited.
Line 120: Why only use results from a single model? Why not use multi-model results from GeoMIP?

Lines 186-187: I don’t quite understand this sentence.
Line 199: The word of ‘accurate’ is not appropriate here.
Line 133: ‘reducing the solar constantor increasing SAI’. Check grammar and spelling here.
Lines 225-226: I don’t understand what ‘SAI is sensitive to global warming’ means.
Line 245: Where are ‘the other three G6 models’?

Citation: https://doi.org/10.5194/egusphere-2023-2904-RC2
- AC3: 'Reply on RC2', Qin’geng Wang, 02 Apr 2024
  
  Dear Referee,
  Thanks for your comments. Please see the attachment for point-by-point responses.
  Best regards
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC3
RC3:
'Comment on egusphere-2023-2904', Anonymous Referee #3, 13 Feb 2024

I think this could be a useful study with some work. I always like creative ways of integrating measurements and models. The analysis is also carefully done and focuses on clearly important issues (extremes).
While I don’t dispute any of the findings, my biggest issue is with the explanations. There is a lot of reporting of the results but not much interpretation other than (sometimes) speculating about mechanisms. Given that the authors have a great deal of climate model output at their disposal, they could look into some of these mechanisms. I would point out specific examples, but this seems to be a general issue in Section 3.
Also, there is a lot of discussion of different indices, but they mostly show the same thing. That’s not a problem, but the way you’re describing them makes it seem like you’re going through a laundry list of indices. I’d like to see more insight. Digging into the results in Table 2 would be interesting. For example, _why_ does CWD not behave like the other indices? What’s special about those two regions that have the opposite sign?
Figure 1 and Section 2.1: Any reason you don’t include the Tibetan plateau?
Lines 169-182: This seems like a long way of saying that you used survival functions, which are a perfectly reasonable thing to use for what you want to do.
Lines 186-187: This is not consistent with my understanding of what field significance does. I would appreciate more description as to what you mean.
Figure 2: Can you add a panel showing the bias as a percent instead of an absolute value?
Lines 225-226: I’m not sure what this means. SAI is sensitive to global warming?
Line 265: Typo (depicting)
Line 293: Aggregated how?
Line 295: Be more specific about “the opposite”. Also, what are 0 values in the table? (I can figure it out, but you need a description.)
Lines 324-325: It’s difficult to put these numbers in context. Is 100 mm a lot for these regions?
Line 341: I don’t know if “effectively mitigates” is the correct phrasing. Be more specific.
Lines 391ff: I’ll be honest, I had a hard time with this entire paragraph. I’m really not sure I understand it.
Line 520: ETCCDI

Citation: https://doi.org/10.5194/egusphere-2023-2904-RC3
- AC4: 'Reply on RC3', Qin’geng Wang, 02 Apr 2024
  
  Dear Referee,
  Thanks for your comments. Please see the attachment for point-by-point responses.
  Best regards
  
  Citation: https://doi.org/10.5194/egusphere-2023-2904-AC4

Peer review completion

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

AR by Qin’geng Wang on behalf of the Authors (30 Apr 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (27 May 2024) by Simone Tilmes

RR by Anonymous Referee #3 (04 Jun 2024)

RR by Anonymous Referee #1 (19 Jun 2024)

Suggestions for revision or reasons for rejection

The authors have worked hard to improve the manuscripts and addressed many of my previous concerns. However, some inconsistencies remain in the text and some fairly major changes are still necessary before this article can be accepted for publication.

I have not checked the ms beyond L270 as my comments are largely repetitive: The point of the G6 simulations is not whether they arrive at a “better” future than SSP5-8.5, a better future would be SSP1.19. Rather the point is whether using SAI to hold temperatures in line with SSP2-45 will have any other unforeseen consequences. Thus all results comparing back to SSP5-85 need to be removed, and statistical significance tests should be between SSP2-45 and G6 simulations. Please revise the remainder of the manuscript to reflect this.

At a general level please ensure you choose either precipitation or rainfall and stick with that terminology. Similarly for “global warming” “anthropogenic global warming” “warming at the global scale”.

I remain unconvinced about the need to present the comparison with APHRODITE data. Your analysis centres around extreme precipitation, for which the models are not appropriate as they overestimate the maximum intensity. Furthermore, you do not (and do not need to!) bias correct the results; and I do not see where else the comparison to observations is used (not line 230-255 as stated in your response). You could instead simply state that the model has been effectively evaluated for adequacy elsewhere citing relevant papers (including your own paper Liang and Haywood 2023!). If you really wish to keep it, I suggest putting a summary of the analysis as an Appendix.

The presentation of the results is still confusing. In all cases you present changes and statistical significance with respect to the control period, but state that SAI is approximately successful. I recommend breaking this up to illustrate both the changes with respect to the control period, and the differences at the end of the century from SSP2-45. Discuss the results with respect to these differences from SSP2-4.5 as that is the target. As the pattern of changes is largely the same for all indices, you don’t need to show the results for the changes relative to control, but could explain that the patterns are similar to those seen in other indices and cite other literature for UKESM/CMIP6 and focus on the effects of SAI.

L24 G6sulfur is
L26 delete “, under future simulations (SSP 5-8.5 and SSP2-4.5),”
L28-29 As stated for the previous version of the ms, this is not appropriate and should be reframed as matching the target FOR THIS MODEL only similar to L31
L30-31 These sentences don’t make sense
L34 Should really highlight that although only one model, small ensemble etc., the hydrological effects are not as beneficial as those indicated for temperature and more research recommended. Leave statements about deployment to policy people.
L43 insert space after “2020,”
L43-52 You could still reduce the level of historical events as the focus of the article is not on these impacts. e.g. start a new paragraph at “Extreme precipitation…” then
“For example severe flooding affected southern, eastern and parts of central China in the summer of 2020 (Jia et al 2020); extremely intense hourly and daily precipitation also occurred over Zhengzhou (southeast? China) in 2021 (Zhao et al, Dong et al). Further extreme precipitation occurred over southern China in 2022, together with flooding in the west of the Yangtze; and finally record-breaking precipitation occurred in 2023 across at least 15 cities and provinces in north-eastern China” Could cite the WMO State of the Climate report here to back up the record-breaker.
WMO (2023) Provisional State of the Climate in 2023. World Meteorological Organization. Available at: https://www.nature.com/articles/nclimate3063 (Accessed: 15 February 2024).
L50 Remove “Although not statistically robust” and “might tentatively”
Dunn et al. (2020) and de Vries et al. (2023) show a robust signal in Rx1day globally.
De Vries, I.E. et al. (2023) ‘Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change’, Earth System Dynamics, 14(1), pp. 81–100. Available at: https://doi.org/10.5194/esd-14-81-2023.
Dunn, R.J.H. et al. (2020) ‘Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3’, Journal of Geophysical Research: Atmospheres, 125(16). Available at: https://doi.org/10.1029/2019JD032263.
L61 Change ‘forecasted’ to ‘projected’
L63 Again this is a policy statement best left to policy makers. The projections show an urgent need to mitigate (I.e. reduce carbon emissions) to avoid worse changes, and to adapt. However, as you go on, some have suggested that climate interventions may also support those actions to further abate the impacts from climate change.
L65 Technically this should be a reference to COP 15, UNFCCC rather than the IPCC special report.
L73-75 Move “Numerous studies….” To L69 before G6Sulfur - to give better background on the idea of a “natural analogue” in the form of volcanoes.
L71 Zarnetske also points out the negative consequences; it is worth making more of that to make this paper a balanced contribution.
L75 New paragraph for “The latest phase of …”
L79 Description of model specifics (I.e. SSP) belongs in the Methods section.
L83 rephrase “Previous studies from a range of modeling experiments innate that SAI will exert….”
L84 This is too generalised. Instead “reduce mean surface air temperature and may reduce global mean precipitation”
Simpson et al. 2019 shows that global mean precipitation is reduced, but locally it may increase. Similarly Pinto et al. (2020) shows that the precipitation responses are not the same across Africa.
L86 change to “when stratospheric sulfur is used to moderate global mean temperatures”
L87 Remove “However, as suggested by some studies,” and change to “can effectively moderate global mean temperature increases, it cannot…”
L92 geoengineering is redundant here.
L93 maybe differences instead of changes? And “between scenarios of projected warming alone, and warming with solar geoengineering”
L98 This should be 2100?
L117 remove “Tier 1” this is confusing.
L125 Define the control period here instead of calling it “Historical”.
L131 maybe “was created from spatial interpolation of gauge…”
L132 change “has been a” to “is a”
L140 include the references that are also in the Table caption here.
L162-190 This section does not describe changes and so doesn’t go with the title. It would be better removed altogether as stated above.
L192 Should be “In all four simulations, most of the region is..”
L193-195 See my general comment and consider removing these lines.
L205 some areas of southern China seem wetter than SSP2-45, and drier in the west for G6. See general comment. Even if statistically significant this still doesn’t categorically show that SAI “effectively mitigates the increase” it demonstrates that it works for this set up in this model.
L212 space between R95p,for
L220 Should be SSP5-8.5
L223 Why is this noteworthy? It seems to be the same location in all four panels.
L227 Presumably the combination of water vapour and south-westerly winds brings more frequent ARs as you demonstrated?
L228 Spelling check all UK or all US throughout.
L231 is this a statistically significant decrease with respect to SSP5-8.5?
L238-252 Figure 5 This would make more sense with SSP2-4.5 - G6sulfur, SSP2-45 - G6solar, and G6sulfur-G6solar because the description is about how areas are wetter than SSP2-45; and your reference point is whether the difference between SSP-2.45 and G6 are statistically distinguishable and what the consequences might be. Update the narrative to match the figures as suggested.
L248 Change “effectively mitigates”
Table 3 as noted above, more compelling would be the difference in results between SSP2-4.5 and G6. Can SAI hold the space without having other consequences?
L270 You still have not included in the article how the CDFs were established for each region!
L286 Four G6 scenarios?

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ED: Publish subject to minor revisions (review by editor) (27 Jun 2024) by Simone Tilmes

AR by Qin’geng Wang on behalf of the Authors (30 Jul 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (17 Sep 2024) by Simone Tilmes

AR by Qin’geng Wang on behalf of the Authors (20 Sep 2024) Author's response Manuscript

Journal article(s) based on this preprint

08 Nov 2024

Projected future changes in extreme precipitation over China under stratospheric aerosol intervention in the UKESM1 climate model

Ou Wang, Ju Liang, Yuchen Gu, Jim M. Haywood, Ying Chen, Chenwei Fang, and Qin'geng Wang

Atmos. Chem. Phys., 24, 12355–12373, https://doi.org/10.5194/acp-24-12355-2024,https://doi.org/10.5194/acp-24-12355-2024, 2024

Short summary

Ou Wang, Ju Liang, Yuchen Gu, Jim M. Haywood, Ying Chen, Chenwei Fang, and Qingeng Wang

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https://doi.org/10.5194/egusphere-2023-2904-supplement

Ou Wang, Ju Liang, Yuchen Gu, Jim M. Haywood, Ying Chen, Chenwei Fang, and Qingeng Wang

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This study investigates the impact of stratospheric aerosol injection (SAI) on extreme precipitation in China by the late 21st century. The effects of SAI (G6sulfur) are compared with simulations from SSP5-8.5, SSP2-4.5, and G6solar. The results indicate that both G6sulfur and G6solar reduce extreme rainfall in eastern China. However, caution is advised due to potential side effects at high latitudes. Further optimization is deemed crucial for the future deployment of SAI.


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