| 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.
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.