Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Tye, Mari R.; Dagon, Katherine; Molina, Maria J.; Richter, Jadwiga H.; Visioni, Daniele; Kravitz, Ben; Tebaldi, Claudia; Tilmes, Simone

doi:https://doi.org/10.5194/egusphere-2022-1

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https://doi.org/10.5194/egusphere-2022-1

Preprints

14 Mar 2022

| 14 Mar 2022

Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Mari R. Tye, Katherine Dagon, Maria J. Molina, Jadwiga H. Richter, Daniele Visioni, Ben Kravitz, Claudia Tebaldi, and Simone Tilmes

Abstract. Extreme weather events have been demonstrated to be increasing in frequency and intensity across the globe and are anticipated to increase further with projected changes in climate. Solar climate intervention strategies, specifically stratospheric aerosol injections (SAI), have the potential to minimise some of the impacts of a changing climate while more robust reductions in greenhouse gas emissions take effect. However, to date little attention has been paid to the possible responses of extreme weather and climate events under climate intervention scenarios. We present an analysis of 16 extreme surface temperature and precipitation indices, and associated vegetation responses, applied to the Geoengineering Large Ensemble (GLENS). GLENS is an ensemble of simulations performed with the Community Earth System Model (CESM1) where SAI is simulated to offset the warming produced by a high emission scenario throughout the 21st century, maintaining surface temperatures at 2020 levels.

GLENS is generally successful at maintaining global mean temperature near 2020 levels, however it does not completely offset some of the projected warming in northern latitudes. Some regions are also projected to cool substantially in comparison to the present day, with the greatest decreases in daytime temperatures. The differential warming/cooling also translates to fewer very hot days but more very hot nights during the summer, and fewer very cold days or nights compared to the current day. Extreme precipitation patterns, for the most part, are projected to reduce in intensity in areas that are wet in the current climate and increase in intensity in dry areas. We also find that the distribution of daily precipitation becomes more consistent with more days with light rain, and fewer very intense events than occur currently. In many regions there is a reduction in the persistence of long dry and wet spells compared to present day. However, asymmetry in the night and day temperatures, together with changes in cloud cover and vegetative responses could exacerbate drying in regions that are already sensitive to drought. Overall, our results suggest that while SAI may ameliorate some of the extreme weather hazards produced by global warming, it would also present some significant differences in the distribution of climate extremes compared to the present day.

Received: 10 Feb 2022 – Discussion started: 14 Mar 2022

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26 Aug 2022

Indices of extremes: geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Mari R. Tye, Katherine Dagon, Maria J. Molina, Jadwiga H. Richter, Daniele Visioni, Ben Kravitz, and Simone Tilmes

Earth Syst. Dynam., 13, 1233–1257, https://doi.org/10.5194/esd-13-1233-2022,https://doi.org/10.5194/esd-13-1233-2022, 2022

Short summary

Mari R. Tye et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-1', Anonymous Referee #1, 29 Apr 2022

This study investigated changes in extreme surface temperature and precipitation indices, and associated vegetation responses under Geoengineering Large Ensemble (GLENS) maintaining global mean surface temperature, the interhemispheric temperature gradient, and the equator-to-pole temperature gradient at 2020 levels. GLENS involves sulfur dioxide injections at four locations (30°N, 15°N, 15°S, and 30°S), however previous stratospheric aerosol geoengineering simulations are almost simulated by equatorial injection. If comparing with the equatorial injection simulations, it would be helpful to understand the responses of extremes indices associated vegetation under GLENS.

Citation: https://doi.org/10.5194/egusphere-2022-1-RC1
- AC1: 'Reply on RC1', Mari Tye, 30 May 2022
  
  Thank you for this suggestion. There is a substantial body of literature devoted to the analysis of the GLENS experiments, one of the conclusions of our analysis of the ETCDDI is that by and large the extremes of daily maximum and minimum temperature and extreme precipitation respond in the same manner as the means of the same variables. This was also demonstrated for other injection strategies It is reasonable to assume that there will be a similar relationship to the mean temperature changes for equatorial simulations, based on similar analyses carried out with different model experiments (Ji et al., 2018).
  As an example, we have carried out the analysis for the coldest night (TNn) using the three member ensemble for the equatorial only injections (Kravitz et al., 2019). As you can see in the figure below comparing TNn (GLENS EC and RCP 8.5 EC) to the mean temperature anomaly (Equatorial minus Baseline from Kravitz et al., 2019, Figure 6), there is little difference in the spatial pattern and magnitude of projected changes of TNn compared to the changes in mean temperature.
  Excerpt from Kravitz et al., (2019) Figure 6
  Comparisons carried out for Coldest night (TNn) in the GLENS-equator only injections attached.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1-AC1
- AC2: 'Reply on RC1', Mari Tye, 30 May 2022
  
  Thank you for this suggestion. There is a substantial body of literature devoted to the analysis of the GLENS experiments, one of the conclusions of our analysis of the ETCDDI is that by and large the extremes of daily maximum and minimum temperature and extreme precipitation respond in the same manner as the means of the same variables. This was also demonstrated for other injection strategies It is reasonable to assume that there will be a similar relationship to the mean temperature changes for equatorial simulations, based on similar analyses carried out with different model experiments (Ji et al., 2018).
  As an example, we have carried out the analysis for the coldest night (TNn) using the three member ensemble for the equatorial only injections (Kravitz et al., 2019). As you can see in the figure below comparing TNn (GLENS EC and RCP 8.5 EC) to the mean temperature anomaly (Equatorial minus Baseline from Kravitz et al., 2019, Figure 6), there is little difference in the spatial pattern and magnitude of projected changes of TNn compared to the changes in mean temperature.
  Excerpt from Kravitz et al., (2019) Figure 6
  Comparisons carried out for Coldest night (TNn) in the GLENS-equator only injections attached.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1-AC2
RC2:
'Comment on egusphere-2022-1', Anonymous Referee #2, 29 Apr 2022

Tye et al. analyse climate model projections under a high-emissions scenario (RCP8.5) and a scenario in which the warming and some aspects of it (gradients) are counterbalanced by stratospheric aerosol injection (GLENS). A novelty of their approach is that they have a single-model large ensemble (20 members). The analysis focuses on extreme climate indices in an early and a late 21-year period.

The study is written in excellent English language and Figures are in very good shape.

The paper does not include much novelty or surprising results. Temperature and precipitation indices approximately behave as expected and as documented in earlier studies. The authors do not exploit much the fact that they dispose of a large ensemble. Basically only the average effects are investigated, not the possible variation between individual weather trajectories. An aspect that is not treated in many other studies is the investigation of vegetation. However, for unclear reason, the authors do not disentangle the role of CO2 (RCP8.5) and of SAI (GLENS minus RCP8.5). This is in contrast to the analysis of temperature and precipitation and would seem to me very useful for vegetation, too.

However, since the study is thoroughly conducted, has a synopsis of the various effect, and also shows quantitative effects (particularly usefully readable in Fig. 5 and 9), it might still be useful to publish the paper. Certainly, it would be beneficial if the authors could demonstrate from observations for the BASE period that the model compares well to observations; so far it is a pure simulation study.

In conclusion, I propose that the authors consider

- showing some evaluation of the BASE period with observational data in terms of extreme indices

- adding the RCP8.5 to the vegetation response analysis,

- as well as a number of specific comments below.

l69 could specifically note the difference in energetic influences of greenhouse gases and aerosols (e.g. Salzmann, Sci. Advances 2016)

l175/176 The values that are not discussed can be omitted from the Table.

l181 is that IPCC AR6? Should be clarified

l230 the label “Feedback” is inconsistent.

l230 It is necessary that some logic is brought to the order of the regions, e.g. clarifying by colour the continents and perhaps a north-south gradient. There is very little discussion of these results in the text.

l236 There is circular reasoning in the sentence. What is the true cause for the impacts?

l241 “consistent” in magnitude or pattern? or just in sign?

l243 Here as well: what is the real unit? days per year? or days per 21-year-period (see below)

l245 Magnitude and in some extended regions, sign

l261 check units?

l263 Where does this discussion of aerosol come from? Do the authors mean, aerosol sedimenting from the stratosphere after injection? Much more discussion on such an effect would be needed.

l268 Fig. 4. The label says “Frequency”, Table 1 says “days” as unit. What is true? Is it days per 21-year-period? Or days per year?

l289 Is this a result at all? I thought to understand that should be true by construction of GLENS?

l291 But is this not the entire meaning of a large ensemble, to be independent of the specific initial conditions for each run?

l296 Explain shading around curves

l303 Eleven years are not exactly half of 21 years.

l313 It would be useful to show this induced east-west SST gradient and discuss its reasons here, since this is fundamental for the subsequent discussion.

l346 Is this also quantitatively the case, i.e. 7 % per K?

l364 Check units

l399 Is this just a qualitative statement (then referring to Clausius-Clapeyron seems useless) or does it hold quantitatively?

l420 Why is the reference (RCP8.5) not shown here? (and not in Fig. 11 either?)

l505 The authors could check in a straightforward manner whether the contraction is also found in their model.

Citation: https://doi.org/10.5194/egusphere-2022-1-RC2
- AC3: 'Reply on RC2', Mari Tye, 30 May 2022
  
  Thank you for your thoughtful review and suggestions. Please see the attached document with our responses.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1-AC3

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-1', Anonymous Referee #1, 29 Apr 2022

This study investigated changes in extreme surface temperature and precipitation indices, and associated vegetation responses under Geoengineering Large Ensemble (GLENS) maintaining global mean surface temperature, the interhemispheric temperature gradient, and the equator-to-pole temperature gradient at 2020 levels. GLENS involves sulfur dioxide injections at four locations (30°N, 15°N, 15°S, and 30°S), however previous stratospheric aerosol geoengineering simulations are almost simulated by equatorial injection. If comparing with the equatorial injection simulations, it would be helpful to understand the responses of extremes indices associated vegetation under GLENS.

Citation: https://doi.org/10.5194/egusphere-2022-1-RC1
- AC1: 'Reply on RC1', Mari Tye, 30 May 2022
  
  Thank you for this suggestion. There is a substantial body of literature devoted to the analysis of the GLENS experiments, one of the conclusions of our analysis of the ETCDDI is that by and large the extremes of daily maximum and minimum temperature and extreme precipitation respond in the same manner as the means of the same variables. This was also demonstrated for other injection strategies It is reasonable to assume that there will be a similar relationship to the mean temperature changes for equatorial simulations, based on similar analyses carried out with different model experiments (Ji et al., 2018).
  As an example, we have carried out the analysis for the coldest night (TNn) using the three member ensemble for the equatorial only injections (Kravitz et al., 2019). As you can see in the figure below comparing TNn (GLENS EC and RCP 8.5 EC) to the mean temperature anomaly (Equatorial minus Baseline from Kravitz et al., 2019, Figure 6), there is little difference in the spatial pattern and magnitude of projected changes of TNn compared to the changes in mean temperature.
  Excerpt from Kravitz et al., (2019) Figure 6
  Comparisons carried out for Coldest night (TNn) in the GLENS-equator only injections attached.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1-AC1
- AC2: 'Reply on RC1', Mari Tye, 30 May 2022
  
  Thank you for this suggestion. There is a substantial body of literature devoted to the analysis of the GLENS experiments, one of the conclusions of our analysis of the ETCDDI is that by and large the extremes of daily maximum and minimum temperature and extreme precipitation respond in the same manner as the means of the same variables. This was also demonstrated for other injection strategies It is reasonable to assume that there will be a similar relationship to the mean temperature changes for equatorial simulations, based on similar analyses carried out with different model experiments (Ji et al., 2018).
  As an example, we have carried out the analysis for the coldest night (TNn) using the three member ensemble for the equatorial only injections (Kravitz et al., 2019). As you can see in the figure below comparing TNn (GLENS EC and RCP 8.5 EC) to the mean temperature anomaly (Equatorial minus Baseline from Kravitz et al., 2019, Figure 6), there is little difference in the spatial pattern and magnitude of projected changes of TNn compared to the changes in mean temperature.
  Excerpt from Kravitz et al., (2019) Figure 6
  Comparisons carried out for Coldest night (TNn) in the GLENS-equator only injections attached.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1-AC2
RC2:
'Comment on egusphere-2022-1', Anonymous Referee #2, 29 Apr 2022

Tye et al. analyse climate model projections under a high-emissions scenario (RCP8.5) and a scenario in which the warming and some aspects of it (gradients) are counterbalanced by stratospheric aerosol injection (GLENS). A novelty of their approach is that they have a single-model large ensemble (20 members). The analysis focuses on extreme climate indices in an early and a late 21-year period.

The study is written in excellent English language and Figures are in very good shape.

The paper does not include much novelty or surprising results. Temperature and precipitation indices approximately behave as expected and as documented in earlier studies. The authors do not exploit much the fact that they dispose of a large ensemble. Basically only the average effects are investigated, not the possible variation between individual weather trajectories. An aspect that is not treated in many other studies is the investigation of vegetation. However, for unclear reason, the authors do not disentangle the role of CO2 (RCP8.5) and of SAI (GLENS minus RCP8.5). This is in contrast to the analysis of temperature and precipitation and would seem to me very useful for vegetation, too.

However, since the study is thoroughly conducted, has a synopsis of the various effect, and also shows quantitative effects (particularly usefully readable in Fig. 5 and 9), it might still be useful to publish the paper. Certainly, it would be beneficial if the authors could demonstrate from observations for the BASE period that the model compares well to observations; so far it is a pure simulation study.

In conclusion, I propose that the authors consider

- showing some evaluation of the BASE period with observational data in terms of extreme indices

- adding the RCP8.5 to the vegetation response analysis,

- as well as a number of specific comments below.

l69 could specifically note the difference in energetic influences of greenhouse gases and aerosols (e.g. Salzmann, Sci. Advances 2016)

l175/176 The values that are not discussed can be omitted from the Table.

l181 is that IPCC AR6? Should be clarified

l230 the label “Feedback” is inconsistent.

l230 It is necessary that some logic is brought to the order of the regions, e.g. clarifying by colour the continents and perhaps a north-south gradient. There is very little discussion of these results in the text.

l236 There is circular reasoning in the sentence. What is the true cause for the impacts?

l241 “consistent” in magnitude or pattern? or just in sign?

l243 Here as well: what is the real unit? days per year? or days per 21-year-period (see below)

l245 Magnitude and in some extended regions, sign

l261 check units?

l263 Where does this discussion of aerosol come from? Do the authors mean, aerosol sedimenting from the stratosphere after injection? Much more discussion on such an effect would be needed.

l268 Fig. 4. The label says “Frequency”, Table 1 says “days” as unit. What is true? Is it days per 21-year-period? Or days per year?

l289 Is this a result at all? I thought to understand that should be true by construction of GLENS?

l291 But is this not the entire meaning of a large ensemble, to be independent of the specific initial conditions for each run?

l296 Explain shading around curves

l303 Eleven years are not exactly half of 21 years.

l313 It would be useful to show this induced east-west SST gradient and discuss its reasons here, since this is fundamental for the subsequent discussion.

l346 Is this also quantitatively the case, i.e. 7 % per K?

l364 Check units

l399 Is this just a qualitative statement (then referring to Clausius-Clapeyron seems useless) or does it hold quantitatively?

l420 Why is the reference (RCP8.5) not shown here? (and not in Fig. 11 either?)

l505 The authors could check in a straightforward manner whether the contraction is also found in their model.

Citation: https://doi.org/10.5194/egusphere-2022-1-RC2
- AC3: 'Reply on RC2', Mari Tye, 30 May 2022
  
  Thank you for your thoughtful review and suggestions. Please see the attached document with our responses.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1-AC3

Peer review completion

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

ED: Reconsider after major revisions (02 Jun 2022) by Somnath Baidya Roy

AR by Mari Tye on behalf of the Authors (14 Jul 2022) Author's response

EF by Sarah Buchmann (18 Jul 2022) Manuscript

EF by Sarah Buchmann (18 Jul 2022) Supplement

EF by Sarah Buchmann (18 Jul 2022) Author's tracked changes

ED: Referee Nomination & Report Request started (19 Jul 2022) by Somnath Baidya Roy

RR by Anonymous Referee #2 (22 Jul 2022)

RR by Anonymous Referee #1 (31 Jul 2022)

ED: Publish as is (08 Aug 2022) by Somnath Baidya Roy

AR by Mari Tye on behalf of the Authors (11 Aug 2022)

Journal article(s) based on this preprint

26 Aug 2022

Indices of extremes: geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Mari R. Tye, Katherine Dagon, Maria J. Molina, Jadwiga H. Richter, Daniele Visioni, Ben Kravitz, and Simone Tilmes

Earth Syst. Dynam., 13, 1233–1257, https://doi.org/10.5194/esd-13-1233-2022,https://doi.org/10.5194/esd-13-1233-2022, 2022

Short summary

Mari R. Tye et al.

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https://doi.org/10.5194/egusphere-2022-1-supplement

Mari R. Tye et al.

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We examined the potential effect of stratospheric aerosol injections (SAI) on extreme temperature and precipitation. SAI may cause daytime temperatures to cool but nighttime to warm. Daytime cooling may occur in all seasons across the globe, with largest decreases in the summer. In contrast, nighttime warming may be greatest at high latitudes in the winter. SAI may reduce the frequency and intensity of extreme rainfall. The combined changes may exacerbate drying over parts of the global South.


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