the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2026
Compensating biases in CCN predictions from composition averaging and neglected surfactant effects
Abstract. Accurate predictions of cloud condensation nuclei (CCN) activation are essential for reducing uncertainties in aerosol-cloud interactions and climate projections. Most large-scale aerosol models represent particles as compositionally averaged internal mixtures and assume constant surface tension of water, neglecting particle-level compositional variability and surfactant-driven reductions in surface tension. Here we use the particle-resolved model WRF-PartMC to quantify how these simplifications affect CCN predictions by comparing particle-resolved (PR) and composition-averaged (Comp) aerosol populations under constant surface tension (CST) and effect surface tension (EST) treatments. Within this framework, PR-EST case provides the most physically detailed reference, and Comp-CST case represents a modal-like aerosol representation in large-scale models. We find this modal-like representation underpredicts CCN by ~19% on average relative to PR-EST reference. This bias reflects two opposing effects: neglecting surfactants suppresses activation, whereas composition averaging shifts activation in both directions depending on particle size and composition. A particle-level decomposition shows that Comp-EST modifies activation through coupled changes in hygroscopicity and surface tension that oppose each other, producing compensating shifts in particle critical supersaturation. These responses produce opposing biases across particle size ranges, with enhanced activation in Aitken mode and suppressed activation in accumulation mode. When EST is included, the remaining bias from composition averaging is substantially reduced, with Comp-EST case differing from the PR-EST reference by ~6% in domain-mean. These results demonstrate simplified aerosol schemes can produce apparently reasonable CCN predictions through compensating errors, even when underlying activation physics is misrepresented. Incorporating effective surface tension therefore offers a practical pathway to reduce structural biases in large-scale models.
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Status: closed
- RC1: 'Comment on egusphere-2026-2354', Anonymous Referee #1, 17 Jun 2026
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RC2: 'Comment on egusphere-2026-2354', Anonymous Referee #2, 01 Jul 2026
# Review: Compensating biases in CCN predictions from composition averaging and neglected surfactant effects
## Overview
This is a valuable paper. The manuscript addresses an important problem in aerosol-cloud interactions: how common simplifications in large-scale aerosol models affect CCN prediction. The main result is clear and useful. A modal-like treatment using composition averaging and constant surface tension can underpredict CCN relative to a more detailed particle-resolved treatment with effective surface tension, and compensating errors can hide part of that discrepancy.
The paper is strongest when it moves beyond bulk CCN differences to explain the particle-level and size-dependent reasons for the bias. This is a valuable contribution because it shows that reasonable agreement in domain-mean CCN does not mean the activation physics are represented correctly. Overall, this is a solid paper with a practical modeling takeaway.
## Comments
### Co-condensation during activation
The manuscript may benefit from a brief discussion of organic co-condensation during humidification and activation. If semi-volatile or intermediate-volatility organics repartition as relative humidity increases, particle composition, organic mass, hygroscopicity, and possibly the surface-active material available at the droplet surface may change during activation. This seems relevant to the paper's central point that composition and surface tension jointly control critical supersaturation.
This does not need to become a new set of simulations, but the authors could acknowledge whether their framework assumes fixed dry-particle composition during activation and discuss how RH-sensitive gas-particle partitioning might affect the magnitude or direction of the reported CCN biases. One relevant recent study is Damha et al. (2023), "Capturing the Relative-Humidity-Sensitive Gas-Particle Partitioning of Organic Aerosols in a 2D Volatility Basis Set," *Geophysical Research Letters*, https://doi.org/10.1029/2023GL106095.
### Clarify CCN versus CDNC interpretation
The discussion of the reported error metric could more clearly distinguish CCN concentration from cloud droplet number concentration (CDNC). I suggest adding a simple clarifying sentence when this metric is introduced, such as: "CCN represents particles that can activate at a specified supersaturation, whereas CDNC represents the number of droplets that actually form in a cloud parcel." This would help prevent readers from interpreting a percent CCN bias as the same percent bias in cloud droplet number.
Citation: https://doi.org/10.5194/egusphere-2026-2354-RC2 -
AC1: 'Comment on egusphere-2026-2354', Xiaotian Xu, 20 Jul 2026
We are pleased to submit a revised version of the manuscript “Compensating biases in CCN predictions from composition averaging and neglected surfactant effects” (egusphere-2026-2354) for consideration in Atmospheric Chemistry and Physics.
We sincerely thank the reviewers for the thorough and constructive comments. We have carefully addressed all comments and believe that the revisions have improved the clarity and interpretation of the manuscript. A detailed response to each comment is provided in the attachment.
We believe that the revised manuscript is now suitable for publication in Atmospheric Chemistry and Physics, and we appreciate the opportunity to revise and resubmit our work.
Status: closed
-
RC1: 'Comment on egusphere-2026-2354', Anonymous Referee #1, 17 Jun 2026
General Comments
This study examines how common simplifications in atmospheric models affect predictions of particles that form cloud droplets by comparing a detailed simulation tracking individual particles to simplified approaches. They found that common simplifications can produce seemingly accurate results because different errors cancel each other. Though results seem accurate, seemingly accurate model outcomes mask incorrect physical processes. Incorporating the effect of surfactants on CCN activation is shown to improve predictions, highlighting a pathway to better atmospheric modeling.
The paper addresses an important topic withing atmospheric chemistry: how simplifications of the aerosol loading affect the outcomes of modeled atmospheric processes (namely cloud condensation nuclei activation). This is an ever-present concern as aerosol-cloud interactions remain a large source of uncertainty for future climate change.
The paper makes a concerted effort to explain the impact of oversimplifying particle composition and surface tension in cloud condensation nuclei predictions, demonstrating that while simplified aerosol schemes may produce reasonable CCN predictions, compensating errors are mostly responsible for these results, and ultimately the underlying mechanism is misrepresented.
Their findings that simplified aerosol schemes do produce reasonable CCN predictions, despite oversimplifying a complex aerosol loading, is surprising given what oversimplificiation overlooks. These results provide valuable insight regarding the applicability of simplified schemes, and an updated solution for scenarios when modeling a more complex scheme is necessary. For the most part, the writing is concise and accessible. Some explanations are lacking in details, but overall the paper is well-written and worthy of publication.
Specific Comments
Figure 1 – You don’t explain in the caption what the graphics at the top right of each subplot depict. I imagine these are meant to be schematics depicting how individual aerosols are represented in each scenario. Is that correct? If that is correct, shouldn’t the effective surface tension in the bottom right plot vary from one particle to the other, similar to subplot (b), which also utilizes an effective surface tension?
Line 114 – I think more details are still needed in this section. How are you obtaining and applying the value for fractional surface coverage of inorganic and organic components for each particle? This value will change depending on the particle and amount of surfactant present, so how are you accounting for this variability in this method? Even if these details are provided in Xu et al., 2026 (a previous publication), I recommend including a more thorough description to answer these questions for the reader, especially since this is a key detail in this manuscript.
Line 130 – Where are you getting the data for the mixing state parameter χ from? How are you able to generate these values for an entire region? Where are you getting the data that is allowing you to calculate this? A more thorough explanation of this parameter and how you generate this data should be included.
Line 130-132 – If χ is only being applied as a diagnostic to interpret prediction errors, how are you obtaining the results such as those in Figure 1? How are you generating a particle-resolved aerosol loading and applying it to predict CCN activation without considering mixing state?
Line 134 – The term “aerosol data” is rather vague. What specific data are you gathering from the model that are being reported in the manuscript?
Line 259-260 – The wording here is very confusing. The language makes it sound like you are only considering a constant surface tension and a particle-resolved aerosol loading in equation 13, but based on the equation itself, you are looking at the effect of incorporating the effective surface tension. I strongly recommend revising this sentence to improve clarity.
Technical Corrections
Line 281 – The introductory phrase should end with a colon, not a period.
Line 354-355 – Because the phrase “composition averaging…surface tension reduction” is an interrupting clause, it should be separated by either em dashes or parentheses, not commas.
Citation: https://doi.org/10.5194/egusphere-2026-2354-RC1 -
RC2: 'Comment on egusphere-2026-2354', Anonymous Referee #2, 01 Jul 2026
# Review: Compensating biases in CCN predictions from composition averaging and neglected surfactant effects
## Overview
This is a valuable paper. The manuscript addresses an important problem in aerosol-cloud interactions: how common simplifications in large-scale aerosol models affect CCN prediction. The main result is clear and useful. A modal-like treatment using composition averaging and constant surface tension can underpredict CCN relative to a more detailed particle-resolved treatment with effective surface tension, and compensating errors can hide part of that discrepancy.
The paper is strongest when it moves beyond bulk CCN differences to explain the particle-level and size-dependent reasons for the bias. This is a valuable contribution because it shows that reasonable agreement in domain-mean CCN does not mean the activation physics are represented correctly. Overall, this is a solid paper with a practical modeling takeaway.
## Comments
### Co-condensation during activation
The manuscript may benefit from a brief discussion of organic co-condensation during humidification and activation. If semi-volatile or intermediate-volatility organics repartition as relative humidity increases, particle composition, organic mass, hygroscopicity, and possibly the surface-active material available at the droplet surface may change during activation. This seems relevant to the paper's central point that composition and surface tension jointly control critical supersaturation.
This does not need to become a new set of simulations, but the authors could acknowledge whether their framework assumes fixed dry-particle composition during activation and discuss how RH-sensitive gas-particle partitioning might affect the magnitude or direction of the reported CCN biases. One relevant recent study is Damha et al. (2023), "Capturing the Relative-Humidity-Sensitive Gas-Particle Partitioning of Organic Aerosols in a 2D Volatility Basis Set," *Geophysical Research Letters*, https://doi.org/10.1029/2023GL106095.
### Clarify CCN versus CDNC interpretation
The discussion of the reported error metric could more clearly distinguish CCN concentration from cloud droplet number concentration (CDNC). I suggest adding a simple clarifying sentence when this metric is introduced, such as: "CCN represents particles that can activate at a specified supersaturation, whereas CDNC represents the number of droplets that actually form in a cloud parcel." This would help prevent readers from interpreting a percent CCN bias as the same percent bias in cloud droplet number.
Citation: https://doi.org/10.5194/egusphere-2026-2354-RC2 -
AC1: 'Comment on egusphere-2026-2354', Xiaotian Xu, 20 Jul 2026
We are pleased to submit a revised version of the manuscript “Compensating biases in CCN predictions from composition averaging and neglected surfactant effects” (egusphere-2026-2354) for consideration in Atmospheric Chemistry and Physics.
We sincerely thank the reviewers for the thorough and constructive comments. We have carefully addressed all comments and believe that the revisions have improved the clarity and interpretation of the manuscript. A detailed response to each comment is provided in the attachment.
We believe that the revised manuscript is now suitable for publication in Atmospheric Chemistry and Physics, and we appreciate the opportunity to revise and resubmit our work.
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- 1
General Comments
This study examines how common simplifications in atmospheric models affect predictions of particles that form cloud droplets by comparing a detailed simulation tracking individual particles to simplified approaches. They found that common simplifications can produce seemingly accurate results because different errors cancel each other. Though results seem accurate, seemingly accurate model outcomes mask incorrect physical processes. Incorporating the effect of surfactants on CCN activation is shown to improve predictions, highlighting a pathway to better atmospheric modeling.
The paper addresses an important topic withing atmospheric chemistry: how simplifications of the aerosol loading affect the outcomes of modeled atmospheric processes (namely cloud condensation nuclei activation). This is an ever-present concern as aerosol-cloud interactions remain a large source of uncertainty for future climate change.
The paper makes a concerted effort to explain the impact of oversimplifying particle composition and surface tension in cloud condensation nuclei predictions, demonstrating that while simplified aerosol schemes may produce reasonable CCN predictions, compensating errors are mostly responsible for these results, and ultimately the underlying mechanism is misrepresented.
Their findings that simplified aerosol schemes do produce reasonable CCN predictions, despite oversimplifying a complex aerosol loading, is surprising given what oversimplificiation overlooks. These results provide valuable insight regarding the applicability of simplified schemes, and an updated solution for scenarios when modeling a more complex scheme is necessary. For the most part, the writing is concise and accessible. Some explanations are lacking in details, but overall the paper is well-written and worthy of publication.
Specific Comments
Figure 1 – You don’t explain in the caption what the graphics at the top right of each subplot depict. I imagine these are meant to be schematics depicting how individual aerosols are represented in each scenario. Is that correct? If that is correct, shouldn’t the effective surface tension in the bottom right plot vary from one particle to the other, similar to subplot (b), which also utilizes an effective surface tension?
Line 114 – I think more details are still needed in this section. How are you obtaining and applying the value for fractional surface coverage of inorganic and organic components for each particle? This value will change depending on the particle and amount of surfactant present, so how are you accounting for this variability in this method? Even if these details are provided in Xu et al., 2026 (a previous publication), I recommend including a more thorough description to answer these questions for the reader, especially since this is a key detail in this manuscript.
Line 130 – Where are you getting the data for the mixing state parameter χ from? How are you able to generate these values for an entire region? Where are you getting the data that is allowing you to calculate this? A more thorough explanation of this parameter and how you generate this data should be included.
Line 130-132 – If χ is only being applied as a diagnostic to interpret prediction errors, how are you obtaining the results such as those in Figure 1? How are you generating a particle-resolved aerosol loading and applying it to predict CCN activation without considering mixing state?
Line 134 – The term “aerosol data” is rather vague. What specific data are you gathering from the model that are being reported in the manuscript?
Line 259-260 – The wording here is very confusing. The language makes it sound like you are only considering a constant surface tension and a particle-resolved aerosol loading in equation 13, but based on the equation itself, you are looking at the effect of incorporating the effective surface tension. I strongly recommend revising this sentence to improve clarity.
Technical Corrections
Line 281 – The introductory phrase should end with a colon, not a period.
Line 354-355 – Because the phrase “composition averaging…surface tension reduction” is an interrupting clause, it should be separated by either em dashes or parentheses, not commas.