the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Aug 2025
Future hydro-climatic changes associated with global warming and stratospheric aerosol intervention scenarios across Central-South Asia and the Tibetan Plateau
Abstract. The Central and South Asian Tibetan Plateau (CSATP) plays a vital role in regulating regional and downstream water availability. However, the region faces growing threats from global warming-induced hydroclimatic changes. This study investigates the hydro-climatic changes in the CSATP region under two future (2071–2100) scenarios of high greenhouse gas (GHG) emissions (SSP5-8.5) and the combined impact of GHG with stratospheric aerosol intervention (SAI), relative to present-day conditions (2015–2035). The temperature, precipitation, real evapotranspiration (RET), available water (AW), runoff, soil moisture (SM), terrestrial water storage (TWS), and leaf area index (LAI) are assessed using model simulations from CESM2-WACCM. These variables exhibit widespread intensification, with significant increases in temperature, precipitation, runoff, and LAI, particularly in eastern central Asia (ECA) and South Asia (SA), accompanied by enhanced seasonal amplitudes and earlier runoff peaks. These shifts indicate stronger seasonality and heightened extremes across the land surface. In contrast, the Geo SSP5-8.5 1.5 (here called Geo-SAI) scenario effectively reduces temperature and dampens the seasonal amplitude of TWS, runoff, RET, and precipitation, thereby counteracting many GHG-emission induced changes. However, Geo-SAI also amplifies seasonal variability in SM and vegetation (LAI), especially in ECA and the Tibetan Plateau (TP), revealing its regionally heterogeneous impacts on land–atmosphere interactions under solar geoengineering. While Geo-SAI does not entirely negate the impacts, it provides a viable pathway for reducing extremes and fostering climate stability in vulnerable regions. These results highlight the potential of SAI to alleviate the adverse hydroclimatic effects of GHG-induced warming in CSATP.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3493', Anonymous Referee #1, 25 Aug 2025
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RC2: 'Comment on egusphere-2025-3493', Anonymous Referee #2, 26 Aug 2025
This manuscript takes model output from CESM2 under SSP5-8.5 and a geoengineering scenario to limit global average surface temperatures to 1.5C above preindustrial using SAI. The authors look at model output for four regions in Asia. My main concern with this manuscript is that the second author, Abolfazl Rezaei, has already published these exact results in the journal JGR Atmospheres (https://doi.org/10.1029/2025JD044163). That article looked at the same variables, in the same regions, under the same climate intervention scenario, as simulated by the same climate model. How is any of this novel? This is self plagiarism, and this manuscript should be rejected.
Other general comments:
It is unclear why the authors chose this scenario and these four regions. SSP5-8.5 with SAI to cool all the way down to 1.5C is, in my opinion, not policy relevant, and many other SAI scenarios on CESM are available. The four regions seem chosen at random, and they lump them into one, calling it “The Central and South Asian Tibetan Plateau”, and speak about it as if it is the same thing as the Tibetan plateau, which it is not. More justification is needed for these decisions. Why this scenario and why these regions? You have already published these results, why are you publishing the same results again?
They do not describe the variables they are analyzing or why they are important. How do changes to these variables impact humans? How is real evapotranspiration, available water, and terrestrial water storage defined?
There are other variables that are more important for predicting future Tibetan plateau water availability than the ones analyzed here, such as snow cover and depth, glacier retreat, lake expansions, monsoon shifts, and permafrost degradation. How can these results be robust or important if they do not include or discuss the main drivers of long term water storage changes in the TP? You discuss some of these things in the intro, and then never again.
Is CLM5 able to represent these processes for these regions, or any of the output variables analyzed here? I have trouble believing that low resolution (1-degree) simulations presented here can properly represent orography, which is important in these mountainous regions. In your intro you state that TP TWS is decreasing by 10.2Gt/yr, but your results show an insignificant decrease to TP TWS of 0.3% under SSP5-8.5. Some kind of model evaluation is needed. Regional high resolution simulations are probably needed.
There is only a soil column in CLM5, and there is no groundwater/aquifer layer. It is misleading to just say “terrestrial water storage” and “available water” without mentioning this.
CLM5 has a river model. Did you look at output for river flow changes?
Why did you look at LAI? What about crop yields? Are crop yields or LAI in the model even impacted by these hydrologic changes? For example, irrigated crops in CLM are given unlimited water supply.
Specific comments:
- Geo SSP5-8.5-1.5 is not a GeoMIP scenario
- In section 2.2, provide a description of CLM5. Has the model been validated for these variables in these regions?
- The conclusion section should make more clear the negative impacts in the TP
- I don’t understand what Figure 3 is showing, is amplitude just the difference between the maximum and minimum value each year? Also, the units are all wrong
- The Figure 4 caption talks about red “T” values that are not present on the plot. The shading would indicate that most changes are not significant, except for temperature. Showing changes to extremes would be more intuitive than showing changes to amplitude
- Move the time series and maps in the supplement to the main text
- Add country borders to the maps, label the color bar units, and define your significance test
Citation: https://doi.org/10.5194/egusphere-2025-3493-RC2 -
CC1: 'Reply on RC2', Abolfazl Rezaei, 27 Aug 2025
We respectfully disagree with the assertion that the present manuscript constitutes self-plagiarism of our previous work in JGR Atmospheres (Rezaei et al., 2025). While both studies use CESM2(WACCM6) outputs, the research scope, spatial focus, variables assessed, and scientific contributions differ substantially:
- JGR Atmospheres (2025) is a global and multi-regional study. It investigated hydrological changes under SAI at both global and continental scales, across 40 IPCC reference regions, and emphasized global means, seasonal amplitudes, and peak timings of three hydrological variables: available water (AW), runoff, and terrestrial water storage (TWS). While EGU manuscript (2025) is a regionally focused study dedicated to the Central-South Asia and Tibetan Plateau (CSATP), a hotspot of hydroclimatic vulnerability. The paper provides detailed subregional analyses (Western Central Asia, Eastern Central Asia, South Asia, Tibetan Plateau), which were not examined in depth in the JGR paper
- JGR paper focused on AW, runoff, TWS, with limited discussion of vegetation (LAI) and snowmelt flux (SMF) as explanatory drivers. Conversely, EGU’s paper expands the scope to eight hydroclimatic variables: temperature, precipitation, real evapotranspiration (RET), AW, runoff, soil moisture (SM), TWS, and LAI. This allows us to assess land–atmosphere interactions (temperature, soil moisture, RET, vegetation dynamics), which were not addressed in JGR.
- JGR paper examined global-regional seasonal amplitude and peak timing of AW, runoff, and TWS. EGU paper extends this by systematically analyzing amplitude shifts and peak timing across all eight variables, emphasizing extreme hydroclimatic variability in CSATP. This includes new insights into monsoon dynamics, soil–snow–vegetation interactions, and extreme event modulation.
- JGR showed that Geo-SAI reduces global runoff despite restoring AW due to vegetation expansion and snowmelt effects. The EGU paper highlights region-specific mechanisms—such as over-suppression of evapotranspiration in ECA/TP, amplified soil moisture seasonality in WCA/TP, and LAI amplification in South Asia—none of which were presented in JGR.
Importantly, in this EGU work, we also plan to include analysis of the ARISE scenario, which was not covered in the JGR paper. ARISE is designed to combine SAI with a moderate emissions baseline (SSP2-4.5) while targeting stabilization of global mean temperatures at 1.5°C above preindustrial levels. This provides a new experimental framework, complementary to the SSP5-8.5 + SAI pathways examined in JGR, and enables us to compare the hydroclimatic consequences of deploying SAI under both high-emission and moderate-emission contexts. This addition significantly enhances the novelty of the present study.
Citation: https://doi.org/10.5194/egusphere-2025-3493-CC1 -
RC3: 'Reply on CC1', Anonymous Referee #2, 27 Aug 2025
Author statements are repeated in quotes, responses are beneath:
“The paper provides detailed subregional analyses (Western Central Asia, Eastern Central Asia, South Asia, Tibetan Plateau), which were not examined in depth in the JGR paper”
In the JGR paper (Figures 2, 3, 4, 5, S7, S8, S9, S10, S15, S16) there is in depth analysis of changes to mean, amplitude, and seasonality of TWS, AW, and runoff in these same regions (Tibetan Plateau, South Asia, Western Central Asia, and Eastern Central Asia), which is the bulk of the analysis in this study.
“EGU’s paper expands the scope to eight hydroclimatic variables: temperature, precipitation, real evapotranspiration (RET), AW, runoff, soil moisture (SM), TWS, and LAI.”
The JGR paper already looks at regional changes to peak temperature, precipitation, real evapotranspiration, AW, runoff, snowmelt flux, TWS, and LAI under SSP5-8.5-1.5C (Figures 6, S12, S13, S15) and discusses these impacts, and Tilmes et al. (2020) looks at regional changes to mean temperature, precipitation, and NPP (similar to LAI).
“This includes new insights into monsoon dynamics, soil–snow–vegetation interactions, and extreme event modulation.”
This manuscript does not mention changes to monsoon dynamics or soil–snow–vegetation interactions in SSP5-8.5-1.5C. Regional changes to amplitude for these variables have already been analyzed in Figure 6 of the JGR paper, as mentioned above.
“Importantly, in this EGU work, we also plan to include analysis of the ARISE scenario, which was not covered in the JGR paper.”
Something that the authors only intend to analyze in future work but have not actually included in the submitted manuscript cannot be counted toward its novelty.
While there is some new analysis included in this manuscript, most of the work largely repeats what was already presented in the JGR paper and reaches the same conclusions, making it insufficiently novel for publication.
Citation: https://doi.org/10.5194/egusphere-2025-3493-RC3
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- 1
Review of Hussain et al.
This paper requires major revisions. It is not clear what the purpose of the paper is. What scientific questions is it addressing? Why did they choose these particular forcing scenarios to study? Why did they only look at one region of the world? They claim it is a climate hotspot, but present no data to support that claim. They present routine plots of some variables from the model output, and find the climate intervention reduces global warming. The variables are not clearly described. They do not connect the variables to particular impacts. This is not new. The paper is wordy, repeating ideas multiple times. The figures need a lot of work, particularly in the supplemental file.
The abstract is very confusing. First of all, “The Central and South Asian Tibetan Plateau,” the words the abstract begins with, is not a thing. After digging into the paper and looking for it in Fig. 1, it turns out that the paper is about four regions, Western Central Asia, Eastern Central Asia, the Tibetan plateau, and South Asia. The second part of this sentence, “plays a vital role in regulating regional and downstream water availability,” is also not correct. Downstream from what? The rivers end up in the ocean and it already has plenty of water. Why was this region chosen?
Second, I got lost in all the acronyms in the abstract. It is best practice to minimize acronyms, because that slows down the reader. And they should never be defined if they are not used again (AW, SA, TP). But if you do use them, they have first be defined (CESM2-WACCM, SSP5-8.5)
Third, what is “real evapotranspiration?” It is a model simulation. How can it be real? I have never heard that term before. And what is “available water?”
Fourth, the conclusions are obvious. If you implemented SAI it would reduce global warming and its impacts. What is new here?
Fifth, why use a very high global warming emissions scenario, such as SSP5-8.5? The world is not on that path.
Sixth, the last two sentences are value-laden and incorrect. The abstract says, “While Geo-SAI doesn’t fully eliminate impacts, it offers a promising route to reduce extremes and enhance climate stability in vulnerable regions. These findings underscore SAI’s potential to ease adverse hydroclimatic effects of GHG-induced warming and support water resource sustainability under high-emission pathways in CSATP.” The problems are:
- Don’t use contractions (doesn’t).
- How can you say that SAI is “a promising route” without considering all the potential risks?
- The last sentence repeats this claim, mention SAI’s potential with no consideration for its negative effects.
Again on line 53 we find “The Central-South Asia and Tibetan Plateau (CSATP).” Tibet is a plateau, but not the rest of the region. What does this mean? There is no Central-South Asia Plateau.
Line 53 claims that this “is a climate hotspot.” The rest of the paragraph addresses only the Tibetan plateau, and not the other regions. And how do you define “hotspot?” Lots of regions will get hotter in the future and have large impacts. How does this region compare to others?
Lines 99-100 repeats this claim “one of the world’s most vulnerable and water-stressed regions.” Where are the data to support this claim? And why keep repeating it?
Again, on lines 117-118, “Collectively, the CSATP represents a highly climate-sensitive and hydro-climatically complex region, increasingly vulnerable to the accelerating impacts of global warming.” Why is it more climate sensitive than any other region? Show the data and stop repeating the same thing.
Section 2.2: Where does the model output for this paper come from? Who did the runs? How were they done? There is a reference to Jones et al. (2022) but this uses the G6sulfur scenario and not one keeping the temperatures 1.5 K above preindustrial. Following the link in the Data Availability section, it turns out that these runs are from Tilmes et al. (2020). Tilmes et al. is erroneously listed twice in the reference list as 2020a and 2020b for some reason, and it has the wrong authors in the reference list, who should be Simone Tilmes, Douglas G. MacMartin, Jan T. M. Lenaerts, Leo van Kampenhout, Laura Muntjewerf, Lili Xia, Cheryl S. Harrison, Kristen M. Krumhardt, Michael J. Mills, Ben Kravitz, and Alan Robock.
Tilmes et al. (2020) included several more scenarios that SSP5-8.5 and Geo RCP8.5 1.5. Why were these chosen for this paper? And Tilmes et al. focused a lot of the analysis on the period 2060-2069, as being more policy relevant. Why is the analysis here only for the end of the century?
What is the source for Fig. 1? The caption is missing any references.
Line 92: “Using outputs from the GeoMIP experimental framework” is wrong. These are not GeoMIP runs. What is the source for them? And “GeoMIP” is not defined.
Lines 108-109: “According to the Intergovernmental Panel on Climate Change (IPCC).” What is the reference to this?
The paper provides no time series of the variables and no maps of the output, except in the supplemental file. Just showing long-term means obscures much of the data. There is plenty of room in the paper for more figures. Why not show some that demonstrate a particular new finding?
Fig. 2 is missing a lot of information. How can you calculate runoff over such large regions? How is soil moisture calculated? Is it plant available? Over what depth? Precipitation and evapotranspiration are rates, not reservoirs, so have the wrong units. How is terrestrial water storage calculated?
Fig. 2: Each set of four panels should you the same scale on the y-axis, so the different regions can be compared. Stretching them to fill each panel makes them all look similar.
Fig. 3: What does amplitude mean? Of what and how is it calculated?
Figs. 4 and 5 show mean seasonal cycles but are plotted incorrectly. January needs to be repeated on the right side of each panel, so that 12 month-to-month changes are plotted and not just 11. Otherwise there is not a complete seasonal cycle.
Fig. 4: It is “seasonal” cycle and not “seasonality.”
Fig. 4: There are no units for any of the variables.
Fig. 4: The caption says there are T values for the month of the trough, but I don’t see any.
Fig. 4 has shading, but it is not explained in the caption.
Fig. 5 has all these same problems.
Lines 156-157: Was evaporation from water included from the ocean grid points? And why does evapotranspiration include evaporation from water, which is, I guess, lakes and rivers?
The analysis only looks at timing and amplitudes of mean seasonal cycles. But aren’t extremes much more important? Don’t they exceed values that are very impactful. The paper needs to include time series and the changing number of extreme values. Certainly soil moisture can show droughts and floods. But the analysis also has to look at the spatial distribution within each region and not just average over large time and space scales.
The supplemental file does have time series and maps, but:
- It needs a title page with a table of contents.
- The figures are a little blurry and small. They need to be larger and fill the entire column from margin to margin. The maps could be almost twice as big by eliminating a lot of the space between the panels and the space between the panels and the margins.
- Why are time series anomalies with respect to the annual mean? They need to be with respect to the mean seasonal cycle of the control. That way, the extremes can be evaluated and the significance can be plotted.
- The anomaly maps are all plotted with different scales. They all need to be plotted with the same color scale so they can be compared.
The color scheme is confusing. Both the minimum and maximum are dark blue. Use distinctive colors. How about red, orange, and yellow for positive numbers and green and blue for negative ones?
The dots should be for the insignificant data so the data we want to see are not covered. How is the significance calculated? What does “Dotted regions indicate the significant change in ensemble members” mean?
There are no units for any of the maps.
Why do the Geo maps cover fewer points than the other maps?
What do we learn from the time series and maps?
Line 188: effectively, not effetely
Line 332: Needs a reference for GLENS.
Line 334-338: This result has already been published by Tilmes et al. (2020) from these runs. This has to be acknowledged. It is not a new result.
The use of “effectiveness” and “over-effective” is not an accurate way to frame climate intervention, I think. The objective is to reduce the negative impacts of global warming, not to bring the climate back to a specific level. This paper looks at lots of metrics, but does not connect them to potentially desirable impacts. How do they affect agriculture or water resources for people? How do they relate to drought or floods? To stronger storms? Yes, they plotted a lot of variables, but what does it actually mean?
Lines 406-410: Again, only the potential advantages of climate intervention are mentioned. In fact, while Carlson et al. (2022) did point out regions where malaria would be reduced with climate intervention, they also found regions where malaria could increase. This paper has to be clear about the potential risks of climate intervention and not just its potential advantages.