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https://doi.org/10.5194/egusphere-2024-3441
© Author(s) 2024. 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-2024-3441
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
15 Nov 2024
| 15 Nov 2024
A Novel Method to Quantify the Uncertainty Contribution of Aerosol-Radiative Interaction Factors
Abstract. The IPCC's assessment report shows that the radiative forcing of aerosol-radiation interactions still involves significant uncertainty. The commonly used method for factor uncertainty estimation is the One-at-A-Time (OAT) method which evaluates factor sensitivity by controlling the change in a single variable while keeping others constant. The outcomes from the OAT method require high data quality to ensure accuracy, and the results are only valid near the selected constant. This study proposes a new method called Constrained Parameter (CP) to quantify the uncertainty contribution of factors in a multi-factor system. This method constrains the uncertainty of a single factor and evaluates its sensitivity by analyzing how this change affects output uncertainty. The most significant advantage of the CP method is that it can be applied to any data distribution, and its results can reflect the overall data characteristics. By comparing the results calculated by the CP method and the OAT method, the proportion of factor interactions in the factor uncertainty contributions can be obtained. As an application of the CP method, it is used to perform a detailed analysis of aerosol-radiation interaction factors’ uncertainty contributions. The top 3 most sensitive factors are the complex refractive index of aerosol shell materials, light-absorbing carbon parameters, and Mie theory parameters. Due to their high sensitivity and low observational precision, these factors represent significant sources of uncertainty in aerosol-radiation interactions. These factors need to be prioritized for operational observation programs and model parameter inputs.
How to cite. He, B. and Zhao, C.: A Novel Method to Quantify the Uncertainty Contribution of Aerosol-Radiative Interaction Factors, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3441, 2024.
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 preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint
23 Jul 2025
A novel method to quantify the uncertainty contribution of aerosol–radiation interaction factors
Bishuo He and Chunsheng Zhao
Atmos. Chem. Phys., 25, 7765–7776, https://doi.org/10.5194/acp-25-7765-2025, https://doi.org/10.5194/acp-25-7765-2025, 2025
Short summary
Short summary
Factor uncertainty analysis helps us understand the impacts of factors on complex systems. Traditional methods have many limitations. This study introduces a new method to measure how each factor contributes to uncertainty. It gains insights into the role of each variable and works for all multi-factor systems. As an application, we analyzed how aerosols affect solar radiation and identified the key factors. These analyses can improve our understanding of the role of aerosols in climate change.
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2024-3441', Anonymous Referee #1, 20 Dec 2024
In this study, the authors attribute uncertainty in the radiative forcing of aerosol-radiation interactions (RFari) by proposing a “constrained parameter” (CP) method, based on Monte-Carlo calculations that calculates sensitivity of RFari to uncertain input parameters. They find that refractive index and size distribution are associated with the largest sensitivities. CP-derived sensitivities differ compared to “one at a time” (OAT) methods for some parameters, although the impact of covariances seems small overall.
The study is interesting because it attributes uncertainty more finely (i.e. to more fundamental parameters) than previous studies. It would however require major revisions before publication because the description of the state of the art is outdated, the discussion of the strength of the CP method needs clarifying, and the description of the authors’ calculations is not sufficiently detailed.
Main comments:
- The authors criticise previous RFari uncertainty attribution work for their lack of account for covariance between uncertain factors and their inability to quantify contributions to total uncertainty. But their literature review stops in 2018, and studies published since then have addressed many of the authors' concerns, including using methods very similar to their proposed CP method. Thorsen et al. (2020, 2021) https://doi.org/10.1175/Jcli-D-19-0669.1 https://doi.org/10.1175/Jcli-D-19-1009.1 and Elsey et al. (2024) https://doi.org/10.5194/acp-24-4065-2024 (and references therein) are particularly relevant. The authors need to place the motivation and results of their study in the current state of the art.
- The list of challenges in section 2.2 is interesting, but it is often unclear why they really challenge uncertainty attribution. Challenges 1 and 5 are real issues but would pose a problem for any uncertainty attribution methodology, including CP. Challenges 2 and 3 are real, but temporal variations do not necessarily translate to variations in uncertainty contributions. Challenges 4 on covariances is real, but crucially depends on which factors are considered and the size of the covariances. So the relevance of each challenge to uncertainty attribution needs to be clarified by extending the discussion.
- Section 3.2 is incomplete and would not allow to reproduce the authors’ calculations. First, it is unclear what the central values given in Table 1 are supposed to represent, and therefore which aerosols the uncertainty analysis is relevant to. A typical industrial aerosol? A typical Chinese aerosol? In addition, the uncertainty ranges (the sigmas) are missing for all parameters, but are obviously crucial to apply both the OAT and CP methods. They must be added to Table 1.
Other comments:
- Abstract, lines 11-12: Need to make the description of the CP method clearer. The current description could apply equally to the OAT method.
- Line 25: RFaci is missing from the list of strong radiative forcing. It is probably much stronger than RHari
- Line 27: Do you mean “has not undergone substantial revisions”? Otherwise, the “Despite significant advances” does not work with the sentence.
- Lines 28-31: Are those model-observation differences still actual? I think the latest IPCC report concluded those disagreements were mostly resolved now.
- Line 25: The list of previous studies is missing recent work that already addresses the shortcomings of older work.
- Section 2.1 is difficult to understand because many variables are not defined: the exponent T, function g, A, the arrow operator, …
- Line 118: Why “linear trend” specifically?
- Caption of Table 1: I suggest deleting “Observation” in the caption, since many data sources are either not from observations or not directly relevant to the observation site.
- Lines 251-253: It would be interesting to also look at the sensitivity of RFari to AOD, SSA and g, to compare with similar studies.
- Section 3.3.2 would work more logically before section 3.3.1. Describe the physical meaning of the results before the OAT/CP differences. Overall the ranking of sensitivity follow expectations, given that Mie theory is used, but I am surprised that coating thickness is important to sensitivity, but kappa is not despite directly affecting aerosol size. Why is that?
Citation: https://doi.org/10.5194/egusphere-2024-3441-RC1 -
AC1: 'Reply on RC1', Chunsheng Zhao, 17 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3441/egusphere-2024-3441-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-3441', Anonymous Referee #2, 27 Jan 2025
The authors present a method for estimating the contribution to RFari uncertainty from the individual parameters that enter into the calculation, such as aerosol optical properties, vertical profiles, kappa, etc. They compare their Constrained Parameter (CP) method to the traditional "one at a time" (OAT) type analysis, and conclude that a more thorough treatment that - crucially - takes into account the correlation between the terms produces an improved ranking of the uncertainty contributions.Overall the study is well documented and presented. My main concern at present is the framing, and with the completeness of some of the analysis presented.The OAT method is used mainly for convenience, as the observational information available on aerosol properties is generally very limited - as the authors acknowledge. The main use for this method therefore seems to be to identify parameters for further study - as the authors also state in the last sentence of their abstract. For the CP method to be a realistic alternative, however, a very high number of assumptions must be made. This is clear from the example the authors show, using the SBDART code to compare RFari uncertainty from the two methods for aerosols "typical of North China".This example is useful, but unfortunately not sufficiently documented. What kind of measurements are these parameters representative of? How consistent are the literature values, the surface observations and the satellite information? What are the uncertainties? As it stands, it simply functions as an illustration that the OAT and CP methods give different results, with little possibility to judge why e.g. the coating thickness seems to be a key difference.Would it be possible to, in addition, provide calculations for a range of aerosol properties, either from the literature of from a set of supersites? Does the ranking of RFari uncertainty contributions stay the same? This would provide very valuable information to the community. As it stands, however, it is difficult to judge whether the conclusions are a broad result from applying a new method, or just a feature of the particular example made. And based on this, I also question whether the study can really support the statement in the abstract (and elsewhere) that "The top 3 most sensitive factors are the complex refractive index of aerosol shell materials, light-absorbing carbon parameters, and Mie theory parameters"?In conclusion, I appreciate the method and the example, but I urge the authors to expand their analysis somewhat such that the results can be generalized, and also to document their example somewhat more thoroughly.Citation: https://doi.org/
10.5194/egusphere-2024-3441-RC2 -
AC2: 'Reply on RC2', Chunsheng Zhao, 17 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3441/egusphere-2024-3441-AC2-supplement.pdf
-
AC3: 'Reply on RC2_2', Chunsheng Zhao, 28 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3441/egusphere-2024-3441-AC3-supplement.pdf
-
AC2: 'Reply on RC2', Chunsheng Zhao, 17 Feb 2025
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2024-3441', Anonymous Referee #1, 20 Dec 2024
In this study, the authors attribute uncertainty in the radiative forcing of aerosol-radiation interactions (RFari) by proposing a “constrained parameter” (CP) method, based on Monte-Carlo calculations that calculates sensitivity of RFari to uncertain input parameters. They find that refractive index and size distribution are associated with the largest sensitivities. CP-derived sensitivities differ compared to “one at a time” (OAT) methods for some parameters, although the impact of covariances seems small overall.
The study is interesting because it attributes uncertainty more finely (i.e. to more fundamental parameters) than previous studies. It would however require major revisions before publication because the description of the state of the art is outdated, the discussion of the strength of the CP method needs clarifying, and the description of the authors’ calculations is not sufficiently detailed.
Main comments:
- The authors criticise previous RFari uncertainty attribution work for their lack of account for covariance between uncertain factors and their inability to quantify contributions to total uncertainty. But their literature review stops in 2018, and studies published since then have addressed many of the authors' concerns, including using methods very similar to their proposed CP method. Thorsen et al. (2020, 2021) https://doi.org/10.1175/Jcli-D-19-0669.1 https://doi.org/10.1175/Jcli-D-19-1009.1 and Elsey et al. (2024) https://doi.org/10.5194/acp-24-4065-2024 (and references therein) are particularly relevant. The authors need to place the motivation and results of their study in the current state of the art.
- The list of challenges in section 2.2 is interesting, but it is often unclear why they really challenge uncertainty attribution. Challenges 1 and 5 are real issues but would pose a problem for any uncertainty attribution methodology, including CP. Challenges 2 and 3 are real, but temporal variations do not necessarily translate to variations in uncertainty contributions. Challenges 4 on covariances is real, but crucially depends on which factors are considered and the size of the covariances. So the relevance of each challenge to uncertainty attribution needs to be clarified by extending the discussion.
- Section 3.2 is incomplete and would not allow to reproduce the authors’ calculations. First, it is unclear what the central values given in Table 1 are supposed to represent, and therefore which aerosols the uncertainty analysis is relevant to. A typical industrial aerosol? A typical Chinese aerosol? In addition, the uncertainty ranges (the sigmas) are missing for all parameters, but are obviously crucial to apply both the OAT and CP methods. They must be added to Table 1.
Other comments:
- Abstract, lines 11-12: Need to make the description of the CP method clearer. The current description could apply equally to the OAT method.
- Line 25: RFaci is missing from the list of strong radiative forcing. It is probably much stronger than RHari
- Line 27: Do you mean “has not undergone substantial revisions”? Otherwise, the “Despite significant advances” does not work with the sentence.
- Lines 28-31: Are those model-observation differences still actual? I think the latest IPCC report concluded those disagreements were mostly resolved now.
- Line 25: The list of previous studies is missing recent work that already addresses the shortcomings of older work.
- Section 2.1 is difficult to understand because many variables are not defined: the exponent T, function g, A, the arrow operator, …
- Line 118: Why “linear trend” specifically?
- Caption of Table 1: I suggest deleting “Observation” in the caption, since many data sources are either not from observations or not directly relevant to the observation site.
- Lines 251-253: It would be interesting to also look at the sensitivity of RFari to AOD, SSA and g, to compare with similar studies.
- Section 3.3.2 would work more logically before section 3.3.1. Describe the physical meaning of the results before the OAT/CP differences. Overall the ranking of sensitivity follow expectations, given that Mie theory is used, but I am surprised that coating thickness is important to sensitivity, but kappa is not despite directly affecting aerosol size. Why is that?
Citation: https://doi.org/10.5194/egusphere-2024-3441-RC1 -
AC1: 'Reply on RC1', Chunsheng Zhao, 17 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3441/egusphere-2024-3441-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-3441', Anonymous Referee #2, 27 Jan 2025
The authors present a method for estimating the contribution to RFari uncertainty from the individual parameters that enter into the calculation, such as aerosol optical properties, vertical profiles, kappa, etc. They compare their Constrained Parameter (CP) method to the traditional "one at a time" (OAT) type analysis, and conclude that a more thorough treatment that - crucially - takes into account the correlation between the terms produces an improved ranking of the uncertainty contributions.Overall the study is well documented and presented. My main concern at present is the framing, and with the completeness of some of the analysis presented.The OAT method is used mainly for convenience, as the observational information available on aerosol properties is generally very limited - as the authors acknowledge. The main use for this method therefore seems to be to identify parameters for further study - as the authors also state in the last sentence of their abstract. For the CP method to be a realistic alternative, however, a very high number of assumptions must be made. This is clear from the example the authors show, using the SBDART code to compare RFari uncertainty from the two methods for aerosols "typical of North China".This example is useful, but unfortunately not sufficiently documented. What kind of measurements are these parameters representative of? How consistent are the literature values, the surface observations and the satellite information? What are the uncertainties? As it stands, it simply functions as an illustration that the OAT and CP methods give different results, with little possibility to judge why e.g. the coating thickness seems to be a key difference.Would it be possible to, in addition, provide calculations for a range of aerosol properties, either from the literature of from a set of supersites? Does the ranking of RFari uncertainty contributions stay the same? This would provide very valuable information to the community. As it stands, however, it is difficult to judge whether the conclusions are a broad result from applying a new method, or just a feature of the particular example made. And based on this, I also question whether the study can really support the statement in the abstract (and elsewhere) that "The top 3 most sensitive factors are the complex refractive index of aerosol shell materials, light-absorbing carbon parameters, and Mie theory parameters"?In conclusion, I appreciate the method and the example, but I urge the authors to expand their analysis somewhat such that the results can be generalized, and also to document their example somewhat more thoroughly.Citation: https://doi.org/
10.5194/egusphere-2024-3441-RC2 -
AC2: 'Reply on RC2', Chunsheng Zhao, 17 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3441/egusphere-2024-3441-AC2-supplement.pdf
-
AC3: 'Reply on RC2_2', Chunsheng Zhao, 28 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3441/egusphere-2024-3441-AC3-supplement.pdf
-
AC2: 'Reply on RC2', Chunsheng Zhao, 17 Feb 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Chunsheng Zhao on behalf of the Authors (17 Feb 2025)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (03 Mar 2025) by Gunnar Myhre
RR by Anonymous Referee #1 (25 Mar 2025)
ED: Publish as is (11 Apr 2025) by Gunnar Myhre
AR by Chunsheng Zhao on behalf of the Authors (11 Apr 2025)
Journal article(s) based on this preprint
23 Jul 2025
A novel method to quantify the uncertainty contribution of aerosol–radiation interaction factors
Bishuo He and Chunsheng Zhao
Atmos. Chem. Phys., 25, 7765–7776, https://doi.org/10.5194/acp-25-7765-2025, https://doi.org/10.5194/acp-25-7765-2025, 2025
Short summary
Short summary
Factor uncertainty analysis helps us understand the impacts of factors on complex systems. Traditional methods have many limitations. This study introduces a new method to measure how each factor contributes to uncertainty. It gains insights into the role of each variable and works for all multi-factor systems. As an application, we analyzed how aerosols affect solar radiation and identified the key factors. These analyses can improve our understanding of the role of aerosols in climate change.
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Bishuo He
Department of atmospheric and oceanic sciences, School of physics, Peking University, Beijing, 100080, China
Chunsheng Zhao
CORRESPONDING AUTHOR
Department of atmospheric and oceanic sciences, School of physics, Peking University, Beijing, 100080, China
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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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(660 KB) - Metadata XML
Short summary
Factor-uncertainty analysis helps us understand their impacts on complex systems. Traditional methods have many limitations. This study introduces a new method to measure how each factor contributes to uncertainty. It gains insights into the role of each variable and works for all multi-factor systems. As an application, we analyzed how aerosols affect solar radiation and identified the key factors. These analyses can improve our understanding of the role of aerosols in climate change.
Factor-uncertainty analysis helps us understand their impacts on complex systems. Traditional...