the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Apr 2024
Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations
Abstract. Aerosols play an important role in the Earth system, but their impact on cloud properties and the resulting radiative forcing of climate remains highly uncertain. The large temporal and spatial variability of a number of aerosols properties and the choice of different ‘pre-industrial’ reference years prevent a concise understanding of basic underlying patterns and trends in aerosols and their impacts on clouds and radiation. In this study, we characterize the spatial patterns and long-term evolution of lower tropospheric aerosols (in terms of regimes) by clustering multiple aerosol properties from preindustrial times to the year 2050 under three different Shared Socioeconomic Pathway (SSP) scenarios, based on a combination of global aerosol model simulations and statistic-based machine learning algorithms. Our analysis suggests that in comparison with the present-day case, lower tropospheric aerosol regimes during preindustrial times are mostly represented by regimes of comparatively clean conditions whereby marked differences between the years 1750 and 1850 emerge due to the growing influence of agriculture and other anthropogenic activities in 1850. Key aspects of the spatial distribution and extent of the aerosol regimes identified in year 2050 differ compared to pre-industrial and present-day, with significant variations resulting from the emission scenario investigated. In 2050, the low emission SSP1-1.9 scenario is the only scenario where the spatial distribution and extent of the aerosol regimes very closely resembles preindustrial conditions whereby the similarity is greater compared to 1850 than 1750. The aerosol regimes for 2050 under SSP3-7.0 closely resemble present-day conditions, but there are some notable regional differences: developed countries tend to shift towards cleaner conditions in future, while the opposite is the case for developing countries. The aerosol regimes for 2050 under SSP2-4.5 represent an intermediate stage between preindustrial times and present-day. Further analysis indicates a north/south difference in the background regime during preindustrial times, and close resemblance of pre-industrial aerosol conditions in the marine regime to present-day conditions in the Southern Hemispheric ocean. Overall, our study allows to extract a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios and to summarize these in terms of aerosol regimes. The approach and findings of this study can be used for designing targeted measurements of different preindustrial-like conditions, and for tailored air pollution mitigation measures in specific regions.
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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.
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Preprint
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1630 KB) - Metadata XML
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Supplement
(485 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Socioeconomic Pathway scenarios. The results provide a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1024', Anonymous Referee #1, 07 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1024/egusphere-2024-1024-RC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-1024', Anonymous Referee #2, 16 May 2024
In this study, the authors improve and apply an aerosol regime analysis published previously (Li et al. 2022, L22) to past and future simulations of aerosols in the ECHAM-MESSY climate model. They find that cleaner regimes dominated in 1750 and 1850 and may dominate again in the future depending on the emission scenario.
The paper is well written, and figures illustrate the discussion well. However, the study suffers from only quantifying the emission-driven changes in aerosol regimes, not looking at climate-driven changes. This limitation has two consequences that severely reduces the insights gained by the study:
- First, there is little point in performing a regime analysis, or even in running a climate model. All the information is already in the emissions. This is particularly obvious in section 3.2, which reaches the same conclusions as the preceding sections but based on emissions only.
- Second, focusing on emissions leads to the probably very misleading conclusion that some future scenarios lead back to preindustrial conditions. That is unlikely: oceans will have warmed, soils and forests will have changed, bare soils will have expanded or shrunk, droughts and other weather extremes will be more frequent. All those changes will affect emissions from land- and ocean-based biogenic aerosols, biomass-burning aerosols, mineral dust, thus affecting the aerosol regimes, as noted in L22 and lines 136.
Still, there are insights to be gained from an emission-only focus, which is why I recommend major revisions. A possible way to address the criticisms above could be to:
- Connect changes in emissions in a given region to changes in aerosol regimes over a wider area. That would involve merging section 3.2 with the rest of the analysis.
- Clearly acknowledge and flag the key limitation of the study in the abstract and add a long discussion to section 4, to elaborate on how climate feedbacks might alter the conclusions. There is an increasing body of work on climate-driven aerosol feedbacks. Chapters 6 (and perhaps 7) of the IPCC AR6 is a good starting point.
Another point of concern comes from Section 2.3. I can just about understand why present-day clusters cannot be used to analyse regimes at other points in time, but there are two aspects that I do not understand:
- First, how does applying a random forest helps? As stated in the introduction, there are few pristine regions in the present day, and they are limited to the aerosols that happen to be emitted in those regions. Don’t you necessarily end up extrapolating out of your training dataset when applying the learning to other times?
- Second, why did you choose present day as the reference? As stated in the introduction, the reference is normally preindustrial, with some variations as to which year or period is used in practice. And, linking to the previous point, how much does the period used for training matter in terms of regime identification?
Other comments:
Lines 114-115: Could note that the IPCC AR6 uses 1750 as a preindustrial reference to assess radiative forcing, but uses 1850-1900 for other aspects, like surface temperature change.
Line 126: “proven” is too strong a word since you do not define what “properly” means. Model skill depends on the level of detail of the comparison, and on the purpose.
Line 132: Would be useful to say here that the time slices are 10-year long. That information only appears on line 149, which is a bit late.
Line 149: So learning is done on annual means only? Wouldn’t you get more information when using seasonal or monthly means, given the large seasonality of many aerosol types?
Line 208: It would be useful to summarise here the regimes according to L22, and especially what “level” means in, for example, “dust dominated level 1”. That information is partly given is section 3.1, which is late.
Lines 315-317: It would be good to remind the reader that the impact of climate change is not considered here, because this kind of conclusion would probably change if climate feedbacks onto aerosol emissions were included.
Line 361: What is the meaning of having two different regimes for the Arctic and Antarctic? It seems to be purely related to magnitude, rather than changes in composition.
Line 490-494: That section needs to be more critical of the emission datasets. This is especially true of biomass-burning and biogenic emissions. We do not know what they were in preindustrial conditions (see for example Marlon et al. 2016) and the future climate-driven changes are unlikely to be properly represented in the CMIP emission datasets. Those uncertainties are crucial to some of the conclusions of the paper, for example paragraph 284-299.
Technical comments:
- Line 161: evaluating -> evaluation
Citation: https://doi.org/10.5194/egusphere-2024-1024-RC2 -
AC1: 'Authors Replies to Referee’s Comments', Jingmin Li, 18 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1024/egusphere-2024-1024-AC1-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1024', Anonymous Referee #1, 07 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1024/egusphere-2024-1024-RC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-1024', Anonymous Referee #2, 16 May 2024
In this study, the authors improve and apply an aerosol regime analysis published previously (Li et al. 2022, L22) to past and future simulations of aerosols in the ECHAM-MESSY climate model. They find that cleaner regimes dominated in 1750 and 1850 and may dominate again in the future depending on the emission scenario.
The paper is well written, and figures illustrate the discussion well. However, the study suffers from only quantifying the emission-driven changes in aerosol regimes, not looking at climate-driven changes. This limitation has two consequences that severely reduces the insights gained by the study:
- First, there is little point in performing a regime analysis, or even in running a climate model. All the information is already in the emissions. This is particularly obvious in section 3.2, which reaches the same conclusions as the preceding sections but based on emissions only.
- Second, focusing on emissions leads to the probably very misleading conclusion that some future scenarios lead back to preindustrial conditions. That is unlikely: oceans will have warmed, soils and forests will have changed, bare soils will have expanded or shrunk, droughts and other weather extremes will be more frequent. All those changes will affect emissions from land- and ocean-based biogenic aerosols, biomass-burning aerosols, mineral dust, thus affecting the aerosol regimes, as noted in L22 and lines 136.
Still, there are insights to be gained from an emission-only focus, which is why I recommend major revisions. A possible way to address the criticisms above could be to:
- Connect changes in emissions in a given region to changes in aerosol regimes over a wider area. That would involve merging section 3.2 with the rest of the analysis.
- Clearly acknowledge and flag the key limitation of the study in the abstract and add a long discussion to section 4, to elaborate on how climate feedbacks might alter the conclusions. There is an increasing body of work on climate-driven aerosol feedbacks. Chapters 6 (and perhaps 7) of the IPCC AR6 is a good starting point.
Another point of concern comes from Section 2.3. I can just about understand why present-day clusters cannot be used to analyse regimes at other points in time, but there are two aspects that I do not understand:
- First, how does applying a random forest helps? As stated in the introduction, there are few pristine regions in the present day, and they are limited to the aerosols that happen to be emitted in those regions. Don’t you necessarily end up extrapolating out of your training dataset when applying the learning to other times?
- Second, why did you choose present day as the reference? As stated in the introduction, the reference is normally preindustrial, with some variations as to which year or period is used in practice. And, linking to the previous point, how much does the period used for training matter in terms of regime identification?
Other comments:
Lines 114-115: Could note that the IPCC AR6 uses 1750 as a preindustrial reference to assess radiative forcing, but uses 1850-1900 for other aspects, like surface temperature change.
Line 126: “proven” is too strong a word since you do not define what “properly” means. Model skill depends on the level of detail of the comparison, and on the purpose.
Line 132: Would be useful to say here that the time slices are 10-year long. That information only appears on line 149, which is a bit late.
Line 149: So learning is done on annual means only? Wouldn’t you get more information when using seasonal or monthly means, given the large seasonality of many aerosol types?
Line 208: It would be useful to summarise here the regimes according to L22, and especially what “level” means in, for example, “dust dominated level 1”. That information is partly given is section 3.1, which is late.
Lines 315-317: It would be good to remind the reader that the impact of climate change is not considered here, because this kind of conclusion would probably change if climate feedbacks onto aerosol emissions were included.
Line 361: What is the meaning of having two different regimes for the Arctic and Antarctic? It seems to be purely related to magnitude, rather than changes in composition.
Line 490-494: That section needs to be more critical of the emission datasets. This is especially true of biomass-burning and biogenic emissions. We do not know what they were in preindustrial conditions (see for example Marlon et al. 2016) and the future climate-driven changes are unlikely to be properly represented in the CMIP emission datasets. Those uncertainties are crucial to some of the conclusions of the paper, for example paragraph 284-299.
Technical comments:
- Line 161: evaluating -> evaluation
Citation: https://doi.org/10.5194/egusphere-2024-1024-RC2 -
AC1: 'Authors Replies to Referee’s Comments', Jingmin Li, 18 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1024/egusphere-2024-1024-AC1-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
Socioeconomic Pathway scenarios. The results provide a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios.
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Jingmin Li
Mattia Righi
Johannes Hendricks
Christof G. Beer
Ulrike Burkhardt
Anja Schmidt
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1630 KB) - Metadata XML
-
Supplement
(485 KB) - BibTeX
- EndNote
- Final revised paper