Future dust concentration over the Middle East and North Africa region under global warming and stratospheric aerosol intervention scenarios
Abstract. The Middle East and North Africa (MENA) is the dustiest region, in the world and understanding the projected changes in the dust concentrations in the region is crucially important. Stratospheric aerosol injection (SAI) geoengineering aims to reduce global warming, by increasing the reflection of a small amount of the incoming solar radiation to space, and hence reducing the global surface temperatures. Using the output from the Geoengineering Large Ensemble Project (GLENS) project, we show a reduction in the dust concentration in the MENA region under both global warming (RCP8.5) and GLENS-SAI scenarios compared to the present-day climate. This reduction over the MENA region is stronger under the SAI scenario, while for dry season (e.g., summer with the strongest dust events), more reduction has been projected for the global warming scenario. The maximum reduction of the dust concentrations in the MENA region (under both the global warming and SAI) is due to the weakening of the dust hotspots emissions from the sources of the Middle East. Further analysis of the differences in the surface temperature, soil water, precipitation, leaf area index, and near surface wind speed provides some insights into the underlying physical mechanisms that determine the changes in the future dust concentrations in the MENA region. We also conduct wavelet analysis using the time series of the monthly, seasonal, and annual climate changes under the SAI simulation to identify the dust relationship with the considered variables. Our findings show that a stronger reduction of the dust concentration in the MENA region under SAI relative to the RCP8.5 scenario is a complex interplay with temperature reduction, precipitation, soil water and leaf area index enhancement, as well as weakening of near surface winds compared to the present-day climate.
Seyed Vahid Mousavi et al.
Status: final response (author comments only)
RC1: 'Comment on acp-2022-370', Anonymous Referee #1, 12 Aug 2022
- AC1: 'Reply on RC1', Seyed Vahid Mousavi, 08 Dec 2022
- AC2: 'Reply on RC1', Seyed Vahid Mousavi, 08 Dec 2022
RC2: 'Referee Comment on acp-2022-370', Anonymous Referee #2, 09 Sep 2022
- AC3: 'Reply on RC2', Seyed Vahid Mousavi, 08 Dec 2022
Seyed Vahid Mousavi et al.
the Socioeconomic Data and Applications Center (SEDECA) https://sedac.ciesin.columbia.edu/
Stratospheric Aerosol Geoengineering Large Ensemble Project - GLENS https://www.cesm.ucar.edu/projects/community-projects/GLENS/
Seyed Vahid Mousavi et al.
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The paper presents a study of future dust concentration in the MENA region under the RCP8.5 scenario and a corresponding geoengineering experiment
designed to keep the global temperature at 2020 levels. The experiments used are from the ensembles generated under the GLENS project. The paper first present a cross-coherence analysis between dust concentrations and variables like temperature, precipitation, soil-moisture etc. It then goes on with a detailed presentation of the changes in the different variables in the RCP8.5 scenario and the geoengineering scenario at the end of the century.
I think the topic is interesting and a study of the changes in the future should be welcomed. However, I think the conclusions from the cross-coherence analysis are questionable and I find that the rest of the paper is too much of an 'atlas' over the changes and don't really answers the questions of what factors that drives the changes in dust concentrations. Because of these concerns, I can't suggest that it is accepted in its present form.
a) The cross-coherence analysis:
I don't find that the method is very well explained in section 2. What are the connections to the axes in Fig. 2? How do you come from the equations to the quantities (amplitude, phase?) shown in the figure? More importantly, I am also confused about the physical interpretation. There are probably annual cycles in all meteorological variables. This means that there will always be coherence between them. As the annual cycle probably is different from a pure sinusoidal, there will also be a signal at 1/2-year. So what do we actually learn from Fig. 2? In the discussion section (l365) it says that the dust is 'substantially influenced' by the changes in the other fields. But I don't think you can conclude that from the analysis. What we learn is only that there is an annual cycle in all the fields including the dust but nothing about the physical interpretation.
b) The rest of the paper seems to me to be too much focusing on presenting the details about the changes in the different fields. I think many of the panels basically shows the same and that the number of plots and panels could be reduced. I really miss some solid analysis and results about what drives the changes in the dust. The dust generally decrease in the RCP8.5 scenario but it decreases further in the geoengineering scenario. Perhaps I am missing something but I could not find an explanation. The correlations in Table 3 could be a beginning, but the physical connection between the variables requires that the trends - which I guess determines most of the correlations here - are removed.
l54: reginal -> regional
l97: So this is an ensemble based on a single climate model? How are the different ensemble members generated?
l103: What is 'interhemispheric temperature gradient'?
l115-130: Is this a new method adopted for the present study? Is it described in the literature before? If it is new perhaps it should be described in more details and more background given. As it is now it is not transparent for me. For example what is a transport bin?
l148: composite analysis? Is this the right word? You calculate the difference of temporal means.
l160, Table 3: Are the correlations averages over all the ensemble members? It should be mentioned in the caption that this is annual means.
As mentioned I have problems with the presentation of the wavelet coherence.
In line 171 why is [(n'-n)dt/s] the complex conjugate? Is omega_0 a constant? If it is how is it selected?
l172: The sentence 'In this approach .. ' seems misplaced here and should be moved down near line 184.
More importantly in Fig. 2 the coherence is shown as function of time (x-axes) and period (y-axes). It is not clear from the text what these
correspond to in the formulas.
Furthermore, the figure caption mention both the power and the phase which is not described in the text. The same goes for the cone of
Eq. 6: Should there not be some smoothing here too?
The discussion of Fig. 2, page 7-8:
It should be pointed out more specifically in the text that Fig. 2 is for SAI. Does it look the same for the RCP8.5? Why focus on the SAI here?
The 22-years variability and variability larger than 16 years seems to be outside the cone of influence. Also, it is not significant in the GWTC. In general the two regions in Fig. 2 look identical to me. I don't think you can say that there are significant differences.
And I don't really see any change after 2040. Perhaps just presenting the GWTC would be better.
l207: 'Out of phase'. Does this mean -180? Is it just difference in sign?
l246: How does this indicate that the model is consistent with observations? There are no observations used in the present study.
Table 3: Why the big difference between RCP8.5 and SAI for temperature correlations? Is this table only discussed in l258?
Section 3 should be split in two or more subsections. Perhaps not start with the coherence?