the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 May 2025
Optimizing CCN predictions through inferred modal aerosol composition – a boreal forest case study
Abstract. The contribution of natural aerosol particles from boreal forests to total aerosol loadings may increases with anticipated reduction in anthropogenic emissions. It is therefore pertinent to understand the cloud-forming potential of these particles. Observational data on aerosol particle number size distribution and chemical composition is required for predicting cloud condensation nuclei (CCN) concentrations. However, long-term online measurements of chemical composition typically provide data on total sub-micron particulate mass, which only represents the larger end of the number size distribution. To bridge this gap, we employed κ-Köhler theory on a multi-year (2016–2020) dataset from Hyytiälä, southern Finland, to investigate improved closure between observed and predicted CCN concentrations by optimizing the size-resolved chemical composition. This optimization improved the CCN closure primarily at supersaturations above 0.5 % where the Aitken mode makes a substantial contribution to the CCN number. The optimization suggested inorganic enrichment in the accumulation mode compared to organic enrichment in the Aitken mode. The mass fractions of inorganics in the two modes vary with season, the greatest difference taking place in winter (+156 % in the accumulation mode as compared with Aitken mode) and smallest in summer (+52 %). These results reflect the contributions from long range transport and chemical cloud processing as well as the pivotal role of organic vapors in facilitating the growth of newly-formed particles towards CCN-sizes. Our study demonstrates the potential for utilizing CCN measurements for inferring information on the parts of the aerosol size distribution that are beyond the reach of traditional online composition measurements.
Competing interests: At least one of the (co-)authors is a mem- ber of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.-
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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.
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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
(2064 KB) - Metadata XML
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Supplement
(2462 KB) - BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1602', Anonymous Referee #1, 28 May 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-1602-RC1 -
AC1: 'Reply on RC1', Ilona Riipinen, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-AC1-supplement.pdf
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AC1: 'Reply on RC1', Ilona Riipinen, 15 Sep 2025
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RC2: 'Comment on egusphere-2025-1602', Anonymous Referee #2, 04 Jun 2025
This manuscript presents a thorough CCN closure study at the SMEAR II station in Hyytiälä, Finland, leveraging long-term observational data (2016–2020) of aerosol size distributions, chemical composition, and CCN concentrations. The authors compare three CCN prediction methods using κ-Köhler theory with different inversion schemes: i)constant kappa parameter, ii) based on bulk chemical-composition, and iii) inferring size-resolved (modal) composition consistent with total mass and CCN observations.
They demonstrate that allowing the Aitken and accumulation modes to have distinct chemical compositions (κ_opt) significantly improves the closure, especially at higher supersaturations (>0.5%), where Aitken mode particles contribute more to CCN. Seasonal trends show organic enrichment in the Aitken mode and a higher inorganic fraction in the accumulation mode, with implications for understanding biogenic contributions to CCN. This study addresses key limitations in CCN prediction models related to the lack of size-resolved chemical composition data and provides a novel methodology to infer this information from inverse modeling. The work is especially timely as climate models increasingly rely on more accurate aerosol-cloud interaction representations, particularly in biogenically influenced environments like boreal forests.
The manuscript is well-structured, the methodology is sound, and the conclusions are meaningful, therefore, the manuscript should be accepted in ACP. However, before publication some minor revisions are recommended to enhance clarity and strengthen the study.
Minor comments:
- Lines 26–27:“This optimization improved the CCN closure primarily at supersaturations above 0.5%…”
Please quantify the improvement in closure—e.g., percent error reduction or NRMSE change—so the reader can assess the magnitude of the model enhancement. - Lines 29–31: “The mass fractions of inorganics in the two modes vary with season...”
Consider reporting the absolute inorganic mass fractions in addition to the percent difference. This would clarify the physical relevance of the enrichment, particularly for radiative implications. - Line 182, Section 2.2
The method assumes the total submicron composition remains constant while redistributing it between modes. Please justify whether this assumption holds during periods of intense NPF or cloud processing, which may differentially affect modal composition. - Line 213, Section 2.1.1:“...more black carbon is also observed, which tends to decrease the overall hygroscopicity.”
Since eBC is treated as size-invariant in the inverse approach, this assumption might bias winter κ estimates. It can be interesting evaluating the sensitivity of κ_opt to this fixed eBC partitioning. - Table 1 and Eq. 4, κ values and densities:
The use of fixed κ for organics (0.12) and organic nitrate might oversimplify temporal variability. Consider discussing the expected range of κ_org from literature (e.g., 0.05–0.2) and its potential influence on κ_bulk accuracy. - Figure 4, Forward and Inverse Closure: The figure would benefit from adding shading or markers to show uncertainty (e.g., interquartile range) of observations. Right now, it's difficult to assess fit quality beyond the median.
- Lines 532–535, κ Discussion:
The increase in Aitken mode organic fraction during summer aligns with biogenic SOA production. This would be strengthened by directly comparing seasonal κ_opt to expected κ values from known BVOC oxidation products (e.g., monoterpenes, sesquiterpenes). - Lines 536, CCN overestimation: The consistent overestimation of CCN concentrations across all supersaturations may point to a limitation of the internal mixing assumption, especially during periods of aerosol complexity. Previous studies suggest that such overestimations are exacerbated during times of mixed aerosol sources (e.g., biogenic + anthropogenic + aged background), where internal mixing assumptions break down. I recommend the authors to explore whether episodes of high bias correlate with increased variability in PNSD, eBC, or chemical markers—and to discuss the implications of external or mixed-state aerosols on the robustness of κ-based predictions.
- Supplementary Table S2:
Although GMB is a useful summary metric, standard deviation or interquartile ranges of GMB values could help assess variability and robustness across time.
Citation: https://doi.org/10.5194/egusphere-2025-1602-RC2 -
AC3: 'Reply on RC2', Ilona Riipinen, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-AC3-supplement.pdf
- Lines 26–27:“This optimization improved the CCN closure primarily at supersaturations above 0.5%…”
-
RC3: 'Comment on egusphere-2025-1602', Anonymous Referee #3, 09 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-RC3-supplement.pdf
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AC2: 'Reply on RC3', Ilona Riipinen, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-AC2-supplement.pdf
-
AC2: 'Reply on RC3', Ilona Riipinen, 15 Sep 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1602', Anonymous Referee #1, 28 May 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Ilona Riipinen, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Ilona Riipinen, 15 Sep 2025
-
RC2: 'Comment on egusphere-2025-1602', Anonymous Referee #2, 04 Jun 2025
This manuscript presents a thorough CCN closure study at the SMEAR II station in Hyytiälä, Finland, leveraging long-term observational data (2016–2020) of aerosol size distributions, chemical composition, and CCN concentrations. The authors compare three CCN prediction methods using κ-Köhler theory with different inversion schemes: i)constant kappa parameter, ii) based on bulk chemical-composition, and iii) inferring size-resolved (modal) composition consistent with total mass and CCN observations.
They demonstrate that allowing the Aitken and accumulation modes to have distinct chemical compositions (κ_opt) significantly improves the closure, especially at higher supersaturations (>0.5%), where Aitken mode particles contribute more to CCN. Seasonal trends show organic enrichment in the Aitken mode and a higher inorganic fraction in the accumulation mode, with implications for understanding biogenic contributions to CCN. This study addresses key limitations in CCN prediction models related to the lack of size-resolved chemical composition data and provides a novel methodology to infer this information from inverse modeling. The work is especially timely as climate models increasingly rely on more accurate aerosol-cloud interaction representations, particularly in biogenically influenced environments like boreal forests.
The manuscript is well-structured, the methodology is sound, and the conclusions are meaningful, therefore, the manuscript should be accepted in ACP. However, before publication some minor revisions are recommended to enhance clarity and strengthen the study.
Minor comments:
- Lines 26–27:“This optimization improved the CCN closure primarily at supersaturations above 0.5%…”
Please quantify the improvement in closure—e.g., percent error reduction or NRMSE change—so the reader can assess the magnitude of the model enhancement. - Lines 29–31: “The mass fractions of inorganics in the two modes vary with season...”
Consider reporting the absolute inorganic mass fractions in addition to the percent difference. This would clarify the physical relevance of the enrichment, particularly for radiative implications. - Line 182, Section 2.2
The method assumes the total submicron composition remains constant while redistributing it between modes. Please justify whether this assumption holds during periods of intense NPF or cloud processing, which may differentially affect modal composition. - Line 213, Section 2.1.1:“...more black carbon is also observed, which tends to decrease the overall hygroscopicity.”
Since eBC is treated as size-invariant in the inverse approach, this assumption might bias winter κ estimates. It can be interesting evaluating the sensitivity of κ_opt to this fixed eBC partitioning. - Table 1 and Eq. 4, κ values and densities:
The use of fixed κ for organics (0.12) and organic nitrate might oversimplify temporal variability. Consider discussing the expected range of κ_org from literature (e.g., 0.05–0.2) and its potential influence on κ_bulk accuracy. - Figure 4, Forward and Inverse Closure: The figure would benefit from adding shading or markers to show uncertainty (e.g., interquartile range) of observations. Right now, it's difficult to assess fit quality beyond the median.
- Lines 532–535, κ Discussion:
The increase in Aitken mode organic fraction during summer aligns with biogenic SOA production. This would be strengthened by directly comparing seasonal κ_opt to expected κ values from known BVOC oxidation products (e.g., monoterpenes, sesquiterpenes). - Lines 536, CCN overestimation: The consistent overestimation of CCN concentrations across all supersaturations may point to a limitation of the internal mixing assumption, especially during periods of aerosol complexity. Previous studies suggest that such overestimations are exacerbated during times of mixed aerosol sources (e.g., biogenic + anthropogenic + aged background), where internal mixing assumptions break down. I recommend the authors to explore whether episodes of high bias correlate with increased variability in PNSD, eBC, or chemical markers—and to discuss the implications of external or mixed-state aerosols on the robustness of κ-based predictions.
- Supplementary Table S2:
Although GMB is a useful summary metric, standard deviation or interquartile ranges of GMB values could help assess variability and robustness across time.
Citation: https://doi.org/10.5194/egusphere-2025-1602-RC2 -
AC3: 'Reply on RC2', Ilona Riipinen, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-AC3-supplement.pdf
- Lines 26–27:“This optimization improved the CCN closure primarily at supersaturations above 0.5%…”
-
RC3: 'Comment on egusphere-2025-1602', Anonymous Referee #3, 09 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-RC3-supplement.pdf
-
AC2: 'Reply on RC3', Ilona Riipinen, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1602/egusphere-2025-1602-AC2-supplement.pdf
-
AC2: 'Reply on RC3', Ilona Riipinen, 15 Sep 2025
Peer review completion
Journal article(s) based on this preprint
Data sets
CCN, size distribution and chemical composition data used to generate the figures Rahul Ranjan https://github.com/rahulranjanaces/Inverse-closure.git
Model code and software
The codes to perform inverse-closure and to generate most of the figures Rahul Ranjan https://github.com/rahulranjanaces/Inverse-closure.git
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Liine Heikkinen
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Tuukka Petäjä
Annica M. L. Ekman
Daniel G. Partridge
Ilona Riipinen
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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