the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Aerosol-cloud impacts on aerosol detrainment and rainout in shallow maritime tropical clouds
Abstract. This study investigates how aerosol-induced changes to cloud properties subsequently influence the overall aerosol budget through changes to detrainment and rainout. We simulated an idealized field of shallow maritime tropical clouds using the Regional Atmospheric Modeling System (RAMS) and varied the aerosol loading and type between model runs to create a 16-member ensemble. The full aerosol budget was tracked over the course of the 48-hour simulation, showing that increasing the aerosol loading leads to an increase in aerosol regeneration and detrainment aloft at the expense of aerosol removal via rainout. Under increased aerosol loadings, cloud droplets are smaller and more likely to evaporate before they form precipitation-sized hydrometeors. As a result, the aerosol particles contained inside these droplets are released into the environment rather than being removed to the surface via rainout. However, the few raindrops which do happen to form under increased aerosol loadings tend to be larger since the cloud water available for collection is divided among fewer raindrops, and thus raindrops experience less evaporation. Thus, in contrast to previous work, we find increases in aerosol loading lead to decreases in aerosol rainout efficiency even without a decrease in the overall precipitation efficiency. We further used tobac, a package for tracking and identifying cloud objects, to identify shifts in the overall cloud population as a function of aerosol loading and type, and found contrasting aerosol effects in shallow cumulus and congestus clouds. Shallow cumulus clouds are more sensitive to the increase in cloud edge/top evaporation with increased aerosol loading, and thereby tend to rain less and remove less aerosol via rainout. On the other hand, larger congestus clouds are more protected from evaporation and are thereby able to benefit from warm-phase invigoration. This leads to an increase in rain rates but not in domain-wide aerosol rainout, as the domain-total rainfall becomes concentrated over a smaller horizontal area. Trends as a function of aerosol loading were remarkably consistent between the different aerosol types tested. These results represent a pathway by which a polluted environment not only has higher aerosol loadings than a pristine one, but is also less able to regulate those loadings by removal processes, instead transporting aerosols to the free troposphere where they remain available for reactivation and further aerosol-cloud interactions.
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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
(2119 KB)
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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
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1406', Anonymous Referee #1, 04 Jan 2023
Leung et al. provide an interesting perspective on aerosol-cloud interactions in warm marine clouds by arguing that aerosol loading not only perturbs the clouds themselves but also the overall aerosol budget. These changes to the aerosol budget are primarily driven by changes to entrainment/detrainment and rainout. Overall I found the paper to be interesting, with nice figures, and it is a good fit for ACP. However, I would like to see more explanation of the experimental setup and analysis of the time-evolution of quantities the before recommending acceptance. Detailed comments below:
L44-45: it would be good to acknowledge here that aerosol changes can also produce changes in atmospheric circulation, which generate global-scale impacts and impacts on different cloud regimes. For example: Dagan (2022, JAMES) and Williams et al. (2022, Nature Climate Change).
L50: It took a few tries for me to understand this part of the sentence, could you please reword? "Aerosol-induced changes to clouds may feed back to how clouds and precipitation influence the aerosol field...". Maybe "Aerosol may alter the relationship between clouds and precipitation and the overall aerosol field..."?
L76: I appreciate that you don't wish to repeat everything about these simulations, but a few more details would be helpful here. For example, do you include a diurnal cycle or does the "diurnal cycle" of Line 79 just refer to a 24hr period? By the sounds of it you included a diurnal cycle in the solar insolation, which I imagine would also alter the aerosol budget through changes in cloudiness? If indeed there is a diurnal cycle in the simulations it would be good to analyse whether these effects vary depending on the time of day (or at least argue why you *don't* do this).
Â
L82: "After initialization, the model was allowed to evolve freely without additional large-scale forcing." Without imposing large-scale forcing, how do you prevent the formation of deep convection in your domain? The lack of a large-scale forcing is confusing to me, why do the authors not just use an established case study like RICO?
L90:Â Do the 'microphysics-radiation' experiments this include the Twomey effect for ice as well as liquid?
Figure 1:Â Very nice schematic, I found it helpful!
L109: A bit pedantic, but I'm not sure if the use of "ensemble" is justified here (or indeed anywhere in the paper) as you only use one set of initial conditions per simulation. Instead, maybe just "multiple simulations were conducted where we varied X and Y...".Â
L120: SSA and other quantities have to defined at a specific wavelength, eg 550nm. Which is it? Also, for the ARI experiments it would be nice to also quote the AOD if the data is available, to more easily compare with other studies with simpler aerosol schemes.
L149: "Qualitatively similar cloud fields develop in all sixteen simulations..." It would be helpful if you could demonstrate this to the reader. For example, a figure showing the time-evolution of domain averaged fields would be helpful to get a sense of the variability over the 48hr period, and perhaps some sense of the spread in domain-avg properties across the experiments too.
L152:Â Again, I'm a bit confused how you don't get more deep convection if you don't impose large-scale temp/moisture tendencies?Â
L162-164:Â Is this just a snapshot at the end of the simulation? How much variability is there in the timeseries? I'm left wondering exactly how representative these changes are.
L169:Â Would the aerosol number not also be conserved? It sounds like if you are just tracking how this is partitioned across the four categories then you could also get a closed budget for aerosol number?Â
L172:Â Would it be possible to show this, at least in the reply? 5% is actually quite large relative to the magnitude of these changes with aerosol loading in Fig. 3. Â Also, I'm confused about why aerosol mass would be "lost" (i.e. not accounted for in your budget) due to dry deposition? Sorry if I'm missing something here.
Figure 3: Please add a '100' marker to the x axis :)
L179: 'simulations' not 'ensemble members'
L196: It is indeed clear from the figures, but I'm still wondering how representative these changes are if they're just calculated from snapshots? I assume there must be a decent bit of temporal variability in the simulated budget quantities?
L236: Why would larger droplets have a smaller surface area? I find the wording confusing.
L241: Regarding precipitation efficiency it would also be good to cite Lutsko et al. (2022, AGU Monographs) and Li et al. (2022, Nature Climate Change). Also it would be nice to discuss whether these results are consistent with the recent study by Dagan (2022, ACP) who also touched on changes in precipitation efficiency with aerosol loading.
Section 4: Just wanted to say that I think this section is great!
Figure 10: Could you add another row above this which shows the baselines radiative cooling rates for each aerosol type? It's difficult to interpret just the changes alone.
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References:
Dagan, 2022 JAMES: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003368
Williams et al, 2022: https://www.nature.com/articles/s41558-022-01415-4Â Â
Lutsko et al 2022; https://www.authorea.com/doi/full/10.1002/essoar.10507822.1
Li et al, 2022: https://www.nature.com/articles/s41558-022-01400-x
Dagan 2022 ACP; https://acp.copernicus.org/articles/22/15767/2022/
Citation: https://doi.org/10.5194/egusphere-2022-1406-RC1 - AC1: 'Reply on RC1', Gabrielle R. Leung, 15 Mar 2023
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RC2: 'Comment on egusphere-2022-1406', Anonymous Referee #2, 25 Jan 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1406/egusphere-2022-1406-RC2-supplement.pdf
- AC2: 'Reply on RC2', Gabrielle R. Leung, 15 Mar 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1406', Anonymous Referee #1, 04 Jan 2023
Leung et al. provide an interesting perspective on aerosol-cloud interactions in warm marine clouds by arguing that aerosol loading not only perturbs the clouds themselves but also the overall aerosol budget. These changes to the aerosol budget are primarily driven by changes to entrainment/detrainment and rainout. Overall I found the paper to be interesting, with nice figures, and it is a good fit for ACP. However, I would like to see more explanation of the experimental setup and analysis of the time-evolution of quantities the before recommending acceptance. Detailed comments below:
L44-45: it would be good to acknowledge here that aerosol changes can also produce changes in atmospheric circulation, which generate global-scale impacts and impacts on different cloud regimes. For example: Dagan (2022, JAMES) and Williams et al. (2022, Nature Climate Change).
L50: It took a few tries for me to understand this part of the sentence, could you please reword? "Aerosol-induced changes to clouds may feed back to how clouds and precipitation influence the aerosol field...". Maybe "Aerosol may alter the relationship between clouds and precipitation and the overall aerosol field..."?
L76: I appreciate that you don't wish to repeat everything about these simulations, but a few more details would be helpful here. For example, do you include a diurnal cycle or does the "diurnal cycle" of Line 79 just refer to a 24hr period? By the sounds of it you included a diurnal cycle in the solar insolation, which I imagine would also alter the aerosol budget through changes in cloudiness? If indeed there is a diurnal cycle in the simulations it would be good to analyse whether these effects vary depending on the time of day (or at least argue why you *don't* do this).
Â
L82: "After initialization, the model was allowed to evolve freely without additional large-scale forcing." Without imposing large-scale forcing, how do you prevent the formation of deep convection in your domain? The lack of a large-scale forcing is confusing to me, why do the authors not just use an established case study like RICO?
L90:Â Do the 'microphysics-radiation' experiments this include the Twomey effect for ice as well as liquid?
Figure 1:Â Very nice schematic, I found it helpful!
L109: A bit pedantic, but I'm not sure if the use of "ensemble" is justified here (or indeed anywhere in the paper) as you only use one set of initial conditions per simulation. Instead, maybe just "multiple simulations were conducted where we varied X and Y...".Â
L120: SSA and other quantities have to defined at a specific wavelength, eg 550nm. Which is it? Also, for the ARI experiments it would be nice to also quote the AOD if the data is available, to more easily compare with other studies with simpler aerosol schemes.
L149: "Qualitatively similar cloud fields develop in all sixteen simulations..." It would be helpful if you could demonstrate this to the reader. For example, a figure showing the time-evolution of domain averaged fields would be helpful to get a sense of the variability over the 48hr period, and perhaps some sense of the spread in domain-avg properties across the experiments too.
L152:Â Again, I'm a bit confused how you don't get more deep convection if you don't impose large-scale temp/moisture tendencies?Â
L162-164:Â Is this just a snapshot at the end of the simulation? How much variability is there in the timeseries? I'm left wondering exactly how representative these changes are.
L169:Â Would the aerosol number not also be conserved? It sounds like if you are just tracking how this is partitioned across the four categories then you could also get a closed budget for aerosol number?Â
L172:Â Would it be possible to show this, at least in the reply? 5% is actually quite large relative to the magnitude of these changes with aerosol loading in Fig. 3. Â Also, I'm confused about why aerosol mass would be "lost" (i.e. not accounted for in your budget) due to dry deposition? Sorry if I'm missing something here.
Figure 3: Please add a '100' marker to the x axis :)
L179: 'simulations' not 'ensemble members'
L196: It is indeed clear from the figures, but I'm still wondering how representative these changes are if they're just calculated from snapshots? I assume there must be a decent bit of temporal variability in the simulated budget quantities?
L236: Why would larger droplets have a smaller surface area? I find the wording confusing.
L241: Regarding precipitation efficiency it would also be good to cite Lutsko et al. (2022, AGU Monographs) and Li et al. (2022, Nature Climate Change). Also it would be nice to discuss whether these results are consistent with the recent study by Dagan (2022, ACP) who also touched on changes in precipitation efficiency with aerosol loading.
Section 4: Just wanted to say that I think this section is great!
Figure 10: Could you add another row above this which shows the baselines radiative cooling rates for each aerosol type? It's difficult to interpret just the changes alone.
Â
Â
Â
Â
Â
Â
Â
Â
Â
References:
Dagan, 2022 JAMES: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003368
Williams et al, 2022: https://www.nature.com/articles/s41558-022-01415-4Â Â
Lutsko et al 2022; https://www.authorea.com/doi/full/10.1002/essoar.10507822.1
Li et al, 2022: https://www.nature.com/articles/s41558-022-01400-x
Dagan 2022 ACP; https://acp.copernicus.org/articles/22/15767/2022/
Citation: https://doi.org/10.5194/egusphere-2022-1406-RC1 - AC1: 'Reply on RC1', Gabrielle R. Leung, 15 Mar 2023
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RC2: 'Comment on egusphere-2022-1406', Anonymous Referee #2, 25 Jan 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1406/egusphere-2022-1406-RC2-supplement.pdf
- AC2: 'Reply on RC2', Gabrielle R. Leung, 15 Mar 2023
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Model code and software
RAMS source code, Python analysis scripts Gabrielle R. Leung, Stephen M. Saleeby https://github.com/grleung/aerobudget
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Cited
1 citations as recorded by crossref.
Gabrielle R. Leung
Stephen M. Saleeby
G. Alexander Sokolowsky
Sean W. Freeman
Susan C. van den Heever
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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