the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Mar 2025
Evolution of aerosol optical depth over China in 2010–2024: increasing importance of meteorological influences
Abstract. Time series of MODIS/MAIAC C6.1 aerosol optical depth (AOD) and model-simulated AOD over China were used to determine contributions of meteorological and anthropogenic effects on aerosol variations on monthly and interannual scales. The study covered the period January 2010–September 2024 with the main focus on five representative areas: NCP, YRD, PRD, HNB and SCB. The time series confirm that emission reduction policy has resulted in the effective reduction of the AOD over China. The large increase of the AOD over the YRD during 2018–2021 shows that meteorological effects have increasingly larger influences on the aerosol load as AOD decreases. During this period, the potential decrease of the AOD over the NCP was effectively cancelled by unfavorable meteorological effects. Meteorological effects, and their variations, are different over each region. For instance, over the NCP, meteorological effects were mostly unfavorable while in contrast, over the PRD, meteorological effects were initially unfavorable but had a strong effect after 2016 when they reinforced anthropogenic effects, resulting in a substantial reduction of the AOD. In addition, the data show a strong AOD minimum in 2022, attributed to favorable anthropogenic effects, over some areas reinforced by favorable meteorological effects. Monthly mean AOD patterns were distinctly different before and after 2016, suggesting that aerosol properties changed in response to emission reduction policy. In summary, this study highlights the complex interplay between meteorological and anthropogenic factors in shaping AOD variations across China and demonstrates the increasing significance of meteorological conditions in modulating China’s AOD.
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Notice on discussion status
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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Preprint
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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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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-880', Anonymous Referee #3, 27 Mar 2025
- AC1: 'Reply on RC1', Xiaoxi Yan, 18 Jun 2025
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RC2: 'Comment on egusphere-2025-880', Anonymous Referee #2, 09 Apr 2025
Review of Manuscript egusphere-2025-880 entitled ‘Evolution of aerosol optical depth over China in 2010-2024: increasing importance of meteorological influences’ by Cheng Fan, Gerrit de Leeuw, Xiaoxi Yan, Jiantao Dong, Hanqing Kang, Chengwei Fang, Zhengqiang Li, Ying Zhang
This manuscript investigates the evolution of AOD in China and the impacts of meteorological and anthropogenic effects across five regions from January 2010 to September 2024, by using MODIS/MAIAC C6.1 data and the CESM CAM5 model, incorporating MERRA-2 meteorological parameters and aerosol emission data as inputs. Processed MAIAC AOD data are used to provide observational insights, while simulated AOD data specifically reflect the influence of meteorological factors. The study focuses on five key regions, i.e., NCP, YRD, PRD, HNB, and SCB. The methodology builds upon a previous study by de Leeuw et al. (2023). The authors extend their AOD dataset from 2010-2021 in de Leeuw et al. (2023) to 2010-2024 in this study. The authors highlight the increasing significance of meteorological factors in AOD variation in recent years.
First of all, I must admit that reading the article is quite challenging due to issues with both logic and language, making me always struggling to grasp the main points of individual paragraphs, which suggests that the manuscript lacks clear organization. Before (required and recommended) re-submission, I strongly encourage the authors to seek professional assistance from a native speaker or a language editing service.
Many paragraphs in Sections 3 and 4 are overly wordy, particularly those discussing long-term AOD variations across different regions. Since this work is an extension of de Leeuw et al. (2023), with the primary addition being three more years of data (2022-2024), it is highly recommended that the authors avoid presenting exhaustive details on all the long-term, short-term, and even occasional monthly anomaly of AOD trends. Such extensive reporting would bring a risk of obscuring the key messages of this study. Instead, the focus should be on the new insights that differ meaningfully from the previous work by de Leeuw et al. (2023). Furthermore, the conclusions regarding the influences of meteorological factors on AOD variations across different regions are not clearly articulated. Even in the abstract part, the use of phrase like ‘for instance’ introduces fragmented examples without conveying coherent or generalizable patterns. To ensure the statistical results are persuasive and the manuscript is readable, substantial revisions are necessary before this manuscript can be reconsidered for publication.
Major comments
- It would be helpful to clarify define the terms “meteorological effect”, “anthropogenic effect”, “favorable meteorological effect”, and “unfavorable meteorological effect”, as their meanings are not always clear (such as in L132-134, L328-330).
- The introduction section spans nearly four pages, which is lengthy and not necessary. It is recommended to condense the general background into one or two concise paragraphs, and then promptly introduce the motivation and objectives of this study.
- Section 2 lacks some important details regarding data processing. For example, the method used to obtain the CMA12 monthly mean AOD should be explained. Additionally, sentences from L142-147 would be more appropriate in the methodology section. For the use of the CESM model, more detailed information should be provided, such as how the model is run. Since the model and input data have different spatial and temporal resolutions, the data processing procedure should be described clearly. It is also suggested to provide a flowchart in Section 2.3 so as to clearly illustrate the modeling process.
- Section 3.1 provides a brief overview of AOD values across different regions but does not describe the long-term characteristics. It is recommended to merge Sections 3.1 and 3.2 as well as to merge Figures 2 and 3.
- Section 3.3 should be significantly shortened, particularly the description of AOD variation, to avoid presenting detailed short-term AOD variation patterns for every region. As mentioned before, the focus should shift toward highlighting new findings, especially those resulting from the additional data covering 2022-2024, which extend the study by de Leeuw et al. (2023). Moreover, this section only presents the AOD variation patterns and the influence of meteorological effects without offering any in-depth analysis. Since all discussions are provided in Section 4, making the readers very difficult to follow. It is suggested that explanations and interpretations for every region be included in the corresponding subsection of Section 3, while Section 4 should focus solely on comparative analysis between regions.
- The logical structure of Section 4 is unclear and often confusing. The discussions frequently jump between different regions, from monthly to annual variations, and between long-term and short-term variation trends. The discussion also lacks a consistent chronological order. Thus, the main conclusions of the study are difficult to discern. Please clarify the core findings more clearly. Consider organizing the section chronologically or dividing it into clearly defined thematic paragraphs to present different points in a more structured way.
Specific comments:
- Abstract, there should be an overall description of AOD evolution for the five areas during the whole studying period of 2010-2024, rather than start from “2018-2021 (L31)” or “after 2016 (L36)”. Besides, some statements are not rigorous and confusing, for instance, “the data show (L37)”, “over some areas (L38)”, please describe in particular. Rephrase the sentence in L31-32, which is confusing.
- L98-100, this sentence seems irrelevant here. In this study, only spaceborne measurements of AOD are applied (rather gas as mentioned here).
- L118, add a reference for ground-based measurements.
- L120, it seems no need to mention trace gases.
- L132-133, provide some basic explanations for how meteorological effects can “enhance” and “reduce” AOD? At least give some examples, such as more precipitation may remove more aerosols (reduce), and more humidity atmosphere can promote hygroscopic growth (enhance)…
- L135, please first mention the time period of de Leeuw et al. (2023), i.e., 2010-2021, and then the 3-year extension 2022-2024
- L138, explain “whole time series”
- L142-143, “somewhat different from”, do you respond to this question later in the manuscript?
- L165-167, this sentence looks like the conclusion of the study and thus should not be shown in the introduction part.
- L228, “with”->“from”
- L233-234, for the evaluation of the MAIAC AOD product, “good enough” is actually enough. Please provide quantitative results from these two references.
- L242, how do you handle the different resolutions of the CESM model and input data? Maybe provide more descriptions.
- L249, not “actual” but from MERRA-2 data
- L262-263, how do you obtain this conclusion? Please add the necessary references.
- L248, “The CESM model simulations were made with anthropogenic emissions fixed at the 2010 level”, please specify “the 2010 level”. Is it the annual mean emission? Or consider using the monthly emission in 2010 to represent the values for the corresponding months of each subsequent year.
- L262-263, “the CESM estimates for desert dust are too high and therefore contributions from desert dust were not included in the AOD calculations”, does this lead to an overestimation or underestimate of anthropogenic contributions?
- L263, the comma should be modified to a full stop.
- L288, “deep minimum”->“plunge”
- L298, what kind of ‘test’?
- L308, what is the meaning of “less effective” and “not effective”
- L322, how observational and simulated AOD be normalized?
- L345-349, reorganized these two sentences, which read a bit strange now.
- L366, “a period of about 6 months”, not clear.
- Figures 4, 6, 8, 10, and 12 have no y-axis titles.
- Figures 5, 7, 9, 11, and 13, the y-axis titles “AOD” is incorrect. Please refer to de Leeuw et al. (2023) (Figure 6 therein)
- L393-394, this sentence is not clear enough. What do you mean “increase”? favorable or unfavorable?
- L413, 423, two “regular AOD pattern” has different meanings.
- L417-418, explain the results in 2014 and 2017 mentioned here.
- Figure 6, two peaks in June of 2012 and 2014 can be related to the emissions from local agricultural straw burning. A lot of related references can be found for these cases.
- L473, please explain what kind of meteorological influence (be specific).
- L478-479, it seems that this sentence can be removed
- L533, you define different ‘regular pattern’ for each region. Please clarify this in sections 3.3.1-3.3.5
- L536, “not regular as…”, please provide some explanations.
- L550-553, no explanations for the observed results.
- L571-581, when comparing the MAIAC AOD and CESM-simulated AOD, please provide some explanations for the differences.
- L608-612, provide some explanations for the regional differences
- Line 760-762, “The high AOD in 2014 has been suggested to be due to anomalous circulation associated with El Niño / La Niña and the strengths of the East Asian summer and winter monsoon effects”, however, there are no descriptions of these El Niño / La Niña effects in the previous analysis.
Citation: https://doi.org/10.5194/egusphere-2025-880-RC2 - AC3: 'Reply on RC2', Xiaoxi Yan, 18 Jun 2025
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RC3: 'Comment on egusphere-2025-880', Anonymous Referee #4, 20 May 2025
General Comments
This manuscript presents an updated analysis of aerosol optical depth (AOD) trends over China from 2010 to 2024, using MODIS/MAIAC C6.1 satellite data and CESM/CAM5 model simulations to distinguish between anthropogenic and meteorological influences. The geographic scope and time span are appropriate, and the study makes effective use of established modeling techniques. However, while the extension of the dataset is valuable, the current manuscript lacks scientific rigor in several sections. Most importantly, the paper does not frame its analysis with clearly stated hypotheses or research questions, making it difficult to evaluate the strength of its conclusions. It also fails to provide a rigorous statistical treatment of the data: there is no formal trend analysis, no uncertainty assessment, and no sensitivity testing of model assumptions. These limitations undermine the robustness of the findings and should be addressed in the new version. Additionally, the manuscript would benefit from improved transparency and stronger engagement with regional climate phenomena (e.g., ENSO, East Asian monsoon) which are only superficially mentioned in the discussion.
Specific Comments
Lack of Hypothesis or Research Questions: the paper would benefit from explicitly stating research hypotheses. Currently, the study lists trends without anchoring them to hypothesis. For example: “Has the relative influence of meteorology increased over time?” or “Does the CESM model successfully replicate observed regional AOD variability under fixed emissions?”
Statistical Rigor and Trend Analysis: while normalized CMA12 filtering is used, the paper does not conduct any formal trend tests (e.g., Mann-Kendall, Sen’s slope). It is missing the uncertainty assessment, confidence interval, or error propagation. As a result, it is unclear whether observed differences (e.g., 2014 vs. 2018 AOD) are statistically significant.
Model Evaluation and Bias Quantification: the CESM model simulations are presented as a basis for attribution to meteorological effects, yet no validation of model performance is provided beyond qualitative agreement. A quantitative comparison between model and satellite AOD (e.g., RMSE, bias, R²) across regions would strengthen credibility.
Sensitivity and Robustness Checks Missing: the fixed-emissions approach assumes that changes in modeled AOD are solely due to meteorological variability. However, no sensitivity analyses are provided to test this assumption. Could other fixed assumptions (e.g., emissions inventory resolution, nudging method) bias the attribution?
No Integration of Large-Scale Climate Drivers: the paper mentions El Niño/La Niña (e.g., L760) only in the discussion, but these large-scale drivers are not analyzed or incorporated in any statistical way. Given the known influence of ENSO and the East Asian monsoon on AOD, this omission is a missed opportunity for deeper interpretation.
Policy Implications Lacking Synthesis: while the abstract references “emission reduction policy,” the conclusions do not explicitly synthesize what the results mean for China’s air quality regulation or international climate targets. Clarifying how meteorological dominance may affect future policy planning would improve the relevance.
Technical Corrections
L248: Clarify whether the “2010 level” refers to monthly averages or annual means.
L263: Fix punctuation — the sentence ends with a comma.
L322: Explain the method of normalization and its justification; currently unclear.
Figures: Add all missing axis labels; clarify units in captions.
Table 1: Consider adding land area or population for context on regional significance.
Citation: https://doi.org/10.5194/egusphere-2025-880-RC3 - AC2: 'Reply on RC3', Xiaoxi Yan, 18 Jun 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-880', Anonymous Referee #3, 27 Mar 2025
The manuscript carefully assessed and determined the contributions of meteorological and anthropogenic effects on aerosol variations on monthly and inter-annual scales of China by taking five key areas and representations based on time series analysis of MODIS/MAIAC C6.1 AOD product and CESM model. It highlighted the complex interplay between meteorological and anthropogenic factors in shaping AOD variations across China and confirmed the increasing significance of meteorological effects in shaping China’s AOD. Overall, the paper is well-written and presents novel findings. However, this manuscript needs some revisions before publication.
Specific comments:
- The evolution of aerosol optical depth over China or East China or typical region, should be clearly indicated in title and abstract of the manuscript.
- Line 47, “NCP, YRD, PRD, HNB and SCB”, Abbreviations should be given their full names when they first appear.
- Line 47, “An aerosol” may be modified as “Aerosol”.
- Line 50, Pay attention to the font size of the word “atmosphere”.
- Figure 1 (line 190-191) shows mainly south-east China, is it suitable to use Southeast Asia other than south-east China for Figure 1’s title? Please confirm.
- Please provide the full name at abbreviations’ first appearance to ensure clarity for readers who may not be familiar with these terms, such as MODIS (line 96), ENSO (line 732), etc.
- Line 201, MODIS sensor also has two channels with a pixel size of 250 m at nadir.
- Line 227-232, some studies show MAIAC AOD have a good validation accuracy in East China (the study area), which can be added in the description.
- The line color of SCB in Figure 2 (line 272), Figure 3 (line 303) is not quite clear, use a darker color?
- Line 296, Pay attention to the font size of the word “tendencies”, “in different regions” should be modified as “in all/each regions”.
- Line 597, what does “These authors” referring to?
- Line 614-615, cite the reference “MEE & General Administration of Quality Supervision Inspection and Quarantine, 2011”, and use the correct citation format of Yan et al. (2023).
Citation: https://doi.org/10.5194/egusphere-2025-880-RC1 - AC1: 'Reply on RC1', Xiaoxi Yan, 18 Jun 2025
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RC2: 'Comment on egusphere-2025-880', Anonymous Referee #2, 09 Apr 2025
Review of Manuscript egusphere-2025-880 entitled ‘Evolution of aerosol optical depth over China in 2010-2024: increasing importance of meteorological influences’ by Cheng Fan, Gerrit de Leeuw, Xiaoxi Yan, Jiantao Dong, Hanqing Kang, Chengwei Fang, Zhengqiang Li, Ying Zhang
This manuscript investigates the evolution of AOD in China and the impacts of meteorological and anthropogenic effects across five regions from January 2010 to September 2024, by using MODIS/MAIAC C6.1 data and the CESM CAM5 model, incorporating MERRA-2 meteorological parameters and aerosol emission data as inputs. Processed MAIAC AOD data are used to provide observational insights, while simulated AOD data specifically reflect the influence of meteorological factors. The study focuses on five key regions, i.e., NCP, YRD, PRD, HNB, and SCB. The methodology builds upon a previous study by de Leeuw et al. (2023). The authors extend their AOD dataset from 2010-2021 in de Leeuw et al. (2023) to 2010-2024 in this study. The authors highlight the increasing significance of meteorological factors in AOD variation in recent years.
First of all, I must admit that reading the article is quite challenging due to issues with both logic and language, making me always struggling to grasp the main points of individual paragraphs, which suggests that the manuscript lacks clear organization. Before (required and recommended) re-submission, I strongly encourage the authors to seek professional assistance from a native speaker or a language editing service.
Many paragraphs in Sections 3 and 4 are overly wordy, particularly those discussing long-term AOD variations across different regions. Since this work is an extension of de Leeuw et al. (2023), with the primary addition being three more years of data (2022-2024), it is highly recommended that the authors avoid presenting exhaustive details on all the long-term, short-term, and even occasional monthly anomaly of AOD trends. Such extensive reporting would bring a risk of obscuring the key messages of this study. Instead, the focus should be on the new insights that differ meaningfully from the previous work by de Leeuw et al. (2023). Furthermore, the conclusions regarding the influences of meteorological factors on AOD variations across different regions are not clearly articulated. Even in the abstract part, the use of phrase like ‘for instance’ introduces fragmented examples without conveying coherent or generalizable patterns. To ensure the statistical results are persuasive and the manuscript is readable, substantial revisions are necessary before this manuscript can be reconsidered for publication.
Major comments
- It would be helpful to clarify define the terms “meteorological effect”, “anthropogenic effect”, “favorable meteorological effect”, and “unfavorable meteorological effect”, as their meanings are not always clear (such as in L132-134, L328-330).
- The introduction section spans nearly four pages, which is lengthy and not necessary. It is recommended to condense the general background into one or two concise paragraphs, and then promptly introduce the motivation and objectives of this study.
- Section 2 lacks some important details regarding data processing. For example, the method used to obtain the CMA12 monthly mean AOD should be explained. Additionally, sentences from L142-147 would be more appropriate in the methodology section. For the use of the CESM model, more detailed information should be provided, such as how the model is run. Since the model and input data have different spatial and temporal resolutions, the data processing procedure should be described clearly. It is also suggested to provide a flowchart in Section 2.3 so as to clearly illustrate the modeling process.
- Section 3.1 provides a brief overview of AOD values across different regions but does not describe the long-term characteristics. It is recommended to merge Sections 3.1 and 3.2 as well as to merge Figures 2 and 3.
- Section 3.3 should be significantly shortened, particularly the description of AOD variation, to avoid presenting detailed short-term AOD variation patterns for every region. As mentioned before, the focus should shift toward highlighting new findings, especially those resulting from the additional data covering 2022-2024, which extend the study by de Leeuw et al. (2023). Moreover, this section only presents the AOD variation patterns and the influence of meteorological effects without offering any in-depth analysis. Since all discussions are provided in Section 4, making the readers very difficult to follow. It is suggested that explanations and interpretations for every region be included in the corresponding subsection of Section 3, while Section 4 should focus solely on comparative analysis between regions.
- The logical structure of Section 4 is unclear and often confusing. The discussions frequently jump between different regions, from monthly to annual variations, and between long-term and short-term variation trends. The discussion also lacks a consistent chronological order. Thus, the main conclusions of the study are difficult to discern. Please clarify the core findings more clearly. Consider organizing the section chronologically or dividing it into clearly defined thematic paragraphs to present different points in a more structured way.
Specific comments:
- Abstract, there should be an overall description of AOD evolution for the five areas during the whole studying period of 2010-2024, rather than start from “2018-2021 (L31)” or “after 2016 (L36)”. Besides, some statements are not rigorous and confusing, for instance, “the data show (L37)”, “over some areas (L38)”, please describe in particular. Rephrase the sentence in L31-32, which is confusing.
- L98-100, this sentence seems irrelevant here. In this study, only spaceborne measurements of AOD are applied (rather gas as mentioned here).
- L118, add a reference for ground-based measurements.
- L120, it seems no need to mention trace gases.
- L132-133, provide some basic explanations for how meteorological effects can “enhance” and “reduce” AOD? At least give some examples, such as more precipitation may remove more aerosols (reduce), and more humidity atmosphere can promote hygroscopic growth (enhance)…
- L135, please first mention the time period of de Leeuw et al. (2023), i.e., 2010-2021, and then the 3-year extension 2022-2024
- L138, explain “whole time series”
- L142-143, “somewhat different from”, do you respond to this question later in the manuscript?
- L165-167, this sentence looks like the conclusion of the study and thus should not be shown in the introduction part.
- L228, “with”->“from”
- L233-234, for the evaluation of the MAIAC AOD product, “good enough” is actually enough. Please provide quantitative results from these two references.
- L242, how do you handle the different resolutions of the CESM model and input data? Maybe provide more descriptions.
- L249, not “actual” but from MERRA-2 data
- L262-263, how do you obtain this conclusion? Please add the necessary references.
- L248, “The CESM model simulations were made with anthropogenic emissions fixed at the 2010 level”, please specify “the 2010 level”. Is it the annual mean emission? Or consider using the monthly emission in 2010 to represent the values for the corresponding months of each subsequent year.
- L262-263, “the CESM estimates for desert dust are too high and therefore contributions from desert dust were not included in the AOD calculations”, does this lead to an overestimation or underestimate of anthropogenic contributions?
- L263, the comma should be modified to a full stop.
- L288, “deep minimum”->“plunge”
- L298, what kind of ‘test’?
- L308, what is the meaning of “less effective” and “not effective”
- L322, how observational and simulated AOD be normalized?
- L345-349, reorganized these two sentences, which read a bit strange now.
- L366, “a period of about 6 months”, not clear.
- Figures 4, 6, 8, 10, and 12 have no y-axis titles.
- Figures 5, 7, 9, 11, and 13, the y-axis titles “AOD” is incorrect. Please refer to de Leeuw et al. (2023) (Figure 6 therein)
- L393-394, this sentence is not clear enough. What do you mean “increase”? favorable or unfavorable?
- L413, 423, two “regular AOD pattern” has different meanings.
- L417-418, explain the results in 2014 and 2017 mentioned here.
- Figure 6, two peaks in June of 2012 and 2014 can be related to the emissions from local agricultural straw burning. A lot of related references can be found for these cases.
- L473, please explain what kind of meteorological influence (be specific).
- L478-479, it seems that this sentence can be removed
- L533, you define different ‘regular pattern’ for each region. Please clarify this in sections 3.3.1-3.3.5
- L536, “not regular as…”, please provide some explanations.
- L550-553, no explanations for the observed results.
- L571-581, when comparing the MAIAC AOD and CESM-simulated AOD, please provide some explanations for the differences.
- L608-612, provide some explanations for the regional differences
- Line 760-762, “The high AOD in 2014 has been suggested to be due to anomalous circulation associated with El Niño / La Niña and the strengths of the East Asian summer and winter monsoon effects”, however, there are no descriptions of these El Niño / La Niña effects in the previous analysis.
Citation: https://doi.org/10.5194/egusphere-2025-880-RC2 - AC3: 'Reply on RC2', Xiaoxi Yan, 18 Jun 2025
-
RC3: 'Comment on egusphere-2025-880', Anonymous Referee #4, 20 May 2025
General Comments
This manuscript presents an updated analysis of aerosol optical depth (AOD) trends over China from 2010 to 2024, using MODIS/MAIAC C6.1 satellite data and CESM/CAM5 model simulations to distinguish between anthropogenic and meteorological influences. The geographic scope and time span are appropriate, and the study makes effective use of established modeling techniques. However, while the extension of the dataset is valuable, the current manuscript lacks scientific rigor in several sections. Most importantly, the paper does not frame its analysis with clearly stated hypotheses or research questions, making it difficult to evaluate the strength of its conclusions. It also fails to provide a rigorous statistical treatment of the data: there is no formal trend analysis, no uncertainty assessment, and no sensitivity testing of model assumptions. These limitations undermine the robustness of the findings and should be addressed in the new version. Additionally, the manuscript would benefit from improved transparency and stronger engagement with regional climate phenomena (e.g., ENSO, East Asian monsoon) which are only superficially mentioned in the discussion.
Specific Comments
Lack of Hypothesis or Research Questions: the paper would benefit from explicitly stating research hypotheses. Currently, the study lists trends without anchoring them to hypothesis. For example: “Has the relative influence of meteorology increased over time?” or “Does the CESM model successfully replicate observed regional AOD variability under fixed emissions?”
Statistical Rigor and Trend Analysis: while normalized CMA12 filtering is used, the paper does not conduct any formal trend tests (e.g., Mann-Kendall, Sen’s slope). It is missing the uncertainty assessment, confidence interval, or error propagation. As a result, it is unclear whether observed differences (e.g., 2014 vs. 2018 AOD) are statistically significant.
Model Evaluation and Bias Quantification: the CESM model simulations are presented as a basis for attribution to meteorological effects, yet no validation of model performance is provided beyond qualitative agreement. A quantitative comparison between model and satellite AOD (e.g., RMSE, bias, R²) across regions would strengthen credibility.
Sensitivity and Robustness Checks Missing: the fixed-emissions approach assumes that changes in modeled AOD are solely due to meteorological variability. However, no sensitivity analyses are provided to test this assumption. Could other fixed assumptions (e.g., emissions inventory resolution, nudging method) bias the attribution?
No Integration of Large-Scale Climate Drivers: the paper mentions El Niño/La Niña (e.g., L760) only in the discussion, but these large-scale drivers are not analyzed or incorporated in any statistical way. Given the known influence of ENSO and the East Asian monsoon on AOD, this omission is a missed opportunity for deeper interpretation.
Policy Implications Lacking Synthesis: while the abstract references “emission reduction policy,” the conclusions do not explicitly synthesize what the results mean for China’s air quality regulation or international climate targets. Clarifying how meteorological dominance may affect future policy planning would improve the relevance.
Technical Corrections
L248: Clarify whether the “2010 level” refers to monthly averages or annual means.
L263: Fix punctuation — the sentence ends with a comma.
L322: Explain the method of normalization and its justification; currently unclear.
Figures: Add all missing axis labels; clarify units in captions.
Table 1: Consider adding land area or population for context on regional significance.
Citation: https://doi.org/10.5194/egusphere-2025-880-RC3 - AC2: 'Reply on RC3', Xiaoxi Yan, 18 Jun 2025
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Cheng Fan
Jiantao Dong
Hanqing Kang
Chengwei Fang
Zhengqiang Li
Ying Zhang
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1840 KB) - Metadata XML
The manuscript carefully assessed and determined the contributions of meteorological and anthropogenic effects on aerosol variations on monthly and inter-annual scales of China by taking five key areas and representations based on time series analysis of MODIS/MAIAC C6.1 AOD product and CESM model. It highlighted the complex interplay between meteorological and anthropogenic factors in shaping AOD variations across China and confirmed the increasing significance of meteorological effects in shaping China’s AOD. Overall, the paper is well-written and presents novel findings. However, this manuscript needs some revisions before publication.
Specific comments: