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https://doi.org/10.5194/egusphere-2025-5149
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-5149
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
24 Oct 2025
| 24 Oct 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Aerosol-deep convection interaction based on joint cell-thermal tracking in Large Eddy Simulations during the TRACER campaign
Abstract. In cumulus clouds, aerosol concentrations control cloud droplet concentrations, modifying cloud radiative properties, precipitation processes, and cloud electrification. However, mechanisms of aerosol-deep convection interactions are not well understood due to complex cloud dynamics and microphysics. We investigate the interaction of aerosols with isolated deep convection using Large Eddy Simulations during the TRacking Aerosol Convection interactions ExpeRiment (TRACER) in the Houston area, using a joint cell-thermal tracking algorithm. Cumulus thermals are droplet generators, since supersaturation and droplet nucleation coincide with thermal centers, where the strongest updrafts occur. Primary ice crystal formation does not take place inside thermals, but at layers where previous thermals detrained moisture. As subsequent thermals containing supercooled droplets penetrate these layers, hail and graupel form at or near these thermals. Higher aerosol concentrations result in higher droplet concentrations that suppress drizzle, delay warm rain processes, and transport more moisture aloft. This increases snow and ice amount, as well as graupel and hail, also associated with more lightning. Also, thermals initiate slightly higher, are slightly larger and faster, suggesting a weak invigoration. We also find more thermals per cell, albeit fewer isolated cells. Convection aggregates more, explaining the lower isolated cell count, and enhancing convection, especially near the end of the 24-hour integration. This non-linear mesoscale feedback is likely triggered by temperature and moisture responses due to aerosol-thermal interactions. Additional time-lagged aerosol-reinialization experiments show that the mesoscale response is the predominant forcing for the invigoration. These changes happen within one day, on a smaller scale than previously suggested.
How to cite. Hernandez-Deckers, D., Matsui, T., Iguchi, T., Brunner, K., Bruning, E. C., van Lier-Walqui, M., Mansell, E. R., Subba, T., Kuang, C., Jensen, M. P., and Braun, S. A.: Aerosol-deep convection interaction based on joint cell-thermal tracking in Large Eddy Simulations during the TRACER campaign, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-5149, 2025.
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Status: open (until 11 Jan 2026)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- CC1: 'Comment on egusphere-2025-5149', Cristian Vraciu, 27 Oct 2025 reply
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RC1: 'Comment on egusphere-2025-5149', Anonymous Referee #1, 15 Dec 2025
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Review of EGUSPHERE-2025-5149: Aerosol-deep convection interaction based on joint cell-thermal tracking in Large Eddy Simulations during the TRACER campaign
General Comments:
In this study, the authors run two simulations at high-resolution (dx=dy=200m) of two cases during the TRACER field campaign. They initialize these simulations with clean and polluted aerosol conditions, based on observations from TRACER. The thermals within these simulations are tracked to assess the impacts of aerosol impacts on deep convective cloud processes on a thermal-centric level. Generally, their figures and discussion were well-presented and easy-to-follow. Their thermal-centric view is very compelling and allows for a more detailed assessment of aerosol impacts on finer scales than prior studies, which adds new insights to specific processes, particularly with respect to the locations of hydrometeors with respect to the thermal centers. This focus on hydrometeors is particularly helpful given that this study also focuses on lightning. The authors’ conclusions generally support results shown in the literature with different modeling frameworks, which is encouraging. While I appreciate the authors’ additional focus on environmental feedbacks, which are important, most of my more significant questions and concerns are related to their analyses and experimental set-up with regards to the environmental feedbacks. Additionally, some other clarifications, including details about their thermal sample composites and the aerosol evolution, would strengthen the manuscript.
Specific Comments:
- L100: I appreciate that the authors constrain their analyses with realistic aerosol conditions from observations.
- L105-107: How well do the log-normal distributions with the parameters listed in Table 1 fit the TRACER observations? A quantification of this fit or an image that shows the fit would be helpful for the reader.
- L126: Can the authors provide an estimate of the vertical grid spacing, as some readers may not be able to easily estimate how 91 model levels translate to vertical distance?
- L149-150: How was this vertical distribution of aerosol decided? Was it also based on observations from TRACER?
- L168: How are the cell merger and splits accounted for. Are they removed? The authors state that the tracks are “refined” based on the evaluation of mergers and splits, but this is vague. Can the authors be more explicit here?
- L170: What is a neighboring feature? This was not defined, so it is hard to understand this limitation on their tracks.
- L206: It is great that the authors consider more than one case to create more robust results.
- L225: The authors use comparative language (e.g., relatively), but it is unclear what they are comparing against.
- L238-240: The authors track thermals within the tracked cells for each of the two cases. For the clean simulations, ~33% of thermals come from 08/04 case and 67% of thermals come from the 08/07 case. However, for the polluted simulations, only 15% of the thermals come from the 08/04 case, while 85% are from the 08/07 case. As such, differences in polluted and clean simulations may be impacted by the different data weights from these cases. Have the authors checked this sampling impact on their results?
- Figure 4: Before diving into the results of clean and polluted thermals, it would be helpful to understand how the aerosol concentration profiles evolved during the simulation. It seems like much of the analysis is focused on the last 6-12 hours of their simulation, after 12-18 hours of model integration. As such, during these last 6-12 hours, do the differences in clean versus polluted match those shown in Table 2 from the initialization, and if not, what are the aerosol differences at these later times? This would be helpful in terms of understanding the actual aerosol perturbation being analyzed in this study and help with the sensitivity experiments described later.
- Figure 5: There seems to be very few thermals that initiate below 2 km (panel d). I understand their reflectivity threshold on cells may miss the early stages of the convective lifecycle; however, for more mature cells, are thermals not originating in the boundary layer? Can the authors explain the lack of thermals below 2 km AGL?
- L252 / L312: The thermal analyses weight their composite by mass flux, which emphasizes the strongest thermals. I am curious how sensitive their findings are to this weighting and whether an equal weighting (e.g., regular mean and median) produces similar results or provides different insights on aerosol impacts on thermals.
- Figure 8: I really like the analyses in and evolution of Figures 4, 7 and 8.
- Figure 11: It seems like having a time series of a reflectivity or rain rate or updraft contour in panels a, b, e, f, such that the time evolution of updraft or precipitation would be more comparable to the time evolution of the Meso-LLC in panels c, d, g, h.
- L373-381: The Aerosol-Cloud-Precipitation-Climate Model Intercomparison Project, which focused on the Houston region with similar sea breeze convection, also found a very similar trend in the environmental response of temperature and moisture at the same vertical levels described in this section. As such, I think it would be helpful to cite that work (Marinescu et al., 2021) here, which will help demonstrate the robustness of this result.
- L391: 0 and 5 km AGL seems like a very deep layer in terms of understanding low-level wind convergence. Why do the authors use 5km AGL as an upper limit? Could other thermal circulations above the bottom (inflow) of the thermal be included in these results due to using such a high limit?
- L394: Are the mesoscale low-level convergence features discussed here referring to cold pools, a sea breeze front or some other low-level convergence boundary? Some clarification here would be helpful.
- L394: Additionally, depending on what the feature is causing the mesoscale low-level conference in this case (e.g., sea-breezes, cold pools), tying in some of the seminal work in terms of aerosol impacts on those specific mesoscale features may be helpful in terms of understanding the physical mechanism at play.
- L394-400: What is the physical mechanism by which aerosol particles impact the clustering differences of convective features within the different simulations? Can the authors speculate about this and/or relate this finding to prior research?
- L397-398: The authors state that under polluted conditions their cells / forcing are more tightly clustered and more consolidated based on a qualitative assessment of Figure 11, which is a snapshot at one time. There are many quantitative assessments of organization in the literature (e.g., Iorg, Lorg), and perhaps these could be used to provide a more quantitative assessment.
- L398: The authors state that the low level convergence is “markedly stronger”. Can the authors quantify this, as it is hard to determine this from Figure 11? Additionally, have the authors considered including convergence in their thermal-centric view plots (e.g., Figure 8), which I thought were very compelling analyses and would show the mesoscale dynamical feedbacks that more directly impact the thermals / updrafts?
- L402: In terms of understanding the mesoscale feedbacks and interactions, the sample size of what has been presented is effectively 2 simulation snapshots. While I recognize the computational limitations of running an ensemble at high resolution, I do think that it may be hard to draw conclusions on the impacts of mesoscale clustering based on this small sample size. This limitation should be clearly stated in this section and the conclusion.
- L407: The authors state that appreciable strengthening only occurs in the late stages, but what do they mean by appreciable? And what is being strengthened? Can they quantify and be more explicit?
- L409: Can they authors be a little clear on what hypothesis they are testing here. They mention a time-sequential impact on the mesoscale environment on the prior line, but what exactly does that mean?
- L409-418: The authors conduct sensitivity simulations where they re-initialize their aerosol concentrations at the beginning of a 2-hour period at the end of their simulations. How does their model deal with such a strong perturbation from one time step to the next? For example, a grid point with clean aerosol conditions is replaced by one with polluted aerosol conditions instantaneously, which could lead to unphysical responses, particularly right after the change. Did the authors consider allowing for some spin-up time to allow for unphysical conditions to be alleviated by the model?
- L409-418: How sensitive are the authors results to the specific timing of these sensitivity experiments? For example, if the aerosol reinitialization occurred at 18 UTC or 20 UTC, as opposed to 22 UTC, how would that impact the results? Similarly, how sensitive are the results to the length of time of analysis after the reinitialization (2 hours)? I assume these results would be sensitive to the lifecycle stage of the convection. I think it would be helpful if the authors can spend more time motivating these experiments since they seem somewhat ill-constrained.
- L428-432. The authors show that the PollRun experiment that was reinitialized with “Clean” aerosol conditions have similar ice and charge conditions as the other PollRun experiments in their domain and time averaged profiles. The authors conclude that this suggests that the mesoscale environment development in the PollRun experiment plays a dominant role in impacting these hydrometeor and electrification profiles since the simulation with reinitialized clean aerosol shows a similar response. However, since these are based on domain mean profiles and the lifetimes of cloud ice can extend for many hours, could this finding be due to the fact that these simulations were run with polluted conditions for ~10 hours before this with polluted conditions. I am having a hard time understanding how the authors can isolate that this is due to the environmental response as opposed to the integrated microphysical response.
- Figure 12: Given the focus on updraft thermals, it would be helpful to include vertical velocity as a panel in Figure 12.
- L433: The authors conduct the same reinitialization experiments for their second case but do not show the results. What is the reasoning for this? The addition of these results would be valuable in terms of improving the robustness of their sensitivity experiment analyses.
- L442: Are the differences/similarities in Figure 12 that are a result of the mesoscale environment due to changes in the temperature/moisture/stability or due to low-level convergence or both? The authors generally use the term “mesoscale environment,” and I think it would be helpful to make it clearer what they are specifically referring to.
- L450: The authors refer to these cases as “golden.” What do they mean by this?
- L483-491: As mentioned above, the ACPC MIP results from Marinescu et al., 2021 also found similar changes to the water vapor and temperature profiles in the environment (their Figure 5, panels i, j and resulting discussion) from a range of models for a similar case study (i.e., same region, type of convection). As such, it should be cited here to strengthen this finding.
- L492-494: Saleeby et al., 2025 focused on tracked updrafts and the microphysical process rates within aerosol perturbation experiments for similar convection in the same region with many different models in the ACPC MIP and could be used here to strengthen this finding about process rates.
- L509-510: Again, this finding about changes in the mesoscale environment using a regional LES domain within a 24-hour integration was also presented in the ACPC MIP studies (Marinescu et al., 2021).
- L511-517. The authors state that “at least, quantifications of changes in the mesoscale environment will be requirement for future MIP activities.” I apologize for bringing this up again, but it seems like the authors may not have been aware that this was done in the MIP studies that they reference here (Marinescu et al., 2021, Saleeby et al., 2025; and van den Heever et al., 2025). As noted in several comments in this review, Marinescu et al., 2021 does quantify changes to the environment within the ACPC MIP model simulations (their Figure 5) and discusses similar mesoscale, environmental feedbacks as discussed in this study to explain the aerosol-impacts on updrafts. Specifically, that study showed both warming and drying in the mesoscale environment boundary layer under more polluted conditions and stated explicitly in the conclusions section that “the consistent CCN-induced response [ in updrafts ] is likely related to an environmental feedback process where the High-CCN simulations have increased environmental instability as a result of warmer boundary layer temperatures and cooler cloud level temperatures.” That study also shows and has discussion about the drier boundary layer environments and moister cloud level environments in polluted conditions. Given that the authors research focuses on the same region and with similar convective features (e.g., scattered, sea breeze convection), the consistent findings between the authors’ work and the ACPC MIP strengthens the conclusion about aerosol impacts on the environment. Can the authors reword this section to accurately reflect this?
Technical Corrections:
- Figure 7 caption – supersaturation with respect to liquid or ice?
- Some of the figures’ text are small (e.g., Figure 9, 12), and I was wondering if the authors could make them larger.
Citation: https://doi.org/10.5194/egusphere-2025-5149-RC1
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Daniel Hernandez-Deckers
CORRESPONDING AUTHOR
Departamento de Geociencias, Universidad Nacional de Colombia, Bogotá D.C., Colombia
Toshihisa Matsui
NASA Goddard Space Flight Center Code 612, Greenbelt, MD USA
Earth System Science Interdisciplanary Center - ESSIC, University of Maryland, College Park, MD, USA
Takamichi Iguchi
NASA Goddard Space Flight Center Code 612, Greenbelt, MD USA
Earth System Science Interdisciplanary Center - ESSIC, University of Maryland, College Park, MD, USA
Kelcy Brunner
National Wind Institute, Texas Tech University, Lubbock, TX, USA
Eric C. Bruning
Department of Geosciences, Texas Tech University, Lubbock, TX, USA
Marcus van Lier-Walqui
NASA Goddard Institute for Space Studies, New York, NY, USA
Center for Climate System Research (CCSR), The Earth Institute, Columbia University, New York, NY, USA
Edward R. Mansell
NOAA National Severe Storms Laboratory, Norman, OK, USA
Tamanna Subba
Brookhaven National Laboratory, Upton, NY, USA
Chongai Kuang
Brookhaven National Laboratory, Upton, NY, USA
Michael P. Jensen
Brookhaven National Laboratory, Upton, NY, USA
Scott A. Braun
NASA Goddard Space Flight Center Code 612, Greenbelt, MD USA
Short summary
Aerosols from air pollution affect weather and climate in various ways. Uncertainties remain on their interactions with clouds, in particular via microphysics (processes related to phase-changes of water that generate rain and lightning). We investigate this with high resolution simulations, focusing on cumulus thermals (the rising bubbles in clouds). We describe the thermals’ roles in these interactions, and identify related mesoscale feedback that enhance convection under polluted conditions.
Aerosols from air pollution affect weather and climate in various ways. Uncertainties remain on...
The statement at line 45: "there has been a growing interest in introducing the thermal concept to better describe cumulus clouds and possibly improve cumulus parameterizations", requires references.