the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Oct 2024
Trends and Drivers of Soluble Iron Deposition from East Asian Dust to the Northwest Pacific: A Springtime Analysis (2001–2017)
Abstract. Recent shifts in dust emissions and atmospheric compositions in East Asia may have a significant impact on the deposition of soluble iron from dust over the Northwest Pacific. This study investigates the trends and driving factors behind this phenomenon during the springs of 2001–2017 using an enhanced version of the Community Atmosphere Model version 6 with comprehensive stratospheric chemistry (CAM6-chem). We improved the model to account for desert dust mineralogy and atmospheric chemical processes that promote iron dissolution, allowing for an in-depth analysis of the evolution of dust iron. Our findings indicate a decreasing trend in dust soluble iron deposition from East Asia to the Northwest Pacific by 2.4 % per year, primarily due to reduced dust emissions driven by declining surface winds over dust source regions. Conversely, the solubility of dust iron showed an increasing trend, rising from 1.5 % in 2001 to 1.7 % in 2017. This increased iron solubility is linked to the acidification of coarse mode aerosols and in-cloud oxalate-ligand-promoted dissolution. Sensitivity model simulations reveal that the increase in anthropogenic NOx emissions, rather than the decrease in SO2, plays a dominant role in enhancing dust aerosol acidity. This study highlights a dual trend: a decrease in the overall deposition of soluble iron from dust, but an increase in the solubility of the iron itself. It underscores the critical roles of both dust emission and atmospheric processing in promoting iron dissolution, which further influences soluble iron deposition and marine ecology.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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.- Preprint
(2430 KB) - Metadata XML
-
Supplement
(686 KB) - BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on egusphere-2024-2293', Anonymous Referee #2, 11 Nov 2024
Summary
This study examines the deposition of soluble iron from dust aerosols using the Community Atmosphere Model version 6 (CAM6-chem). CAM6-chem has been developed here to include desert dust mineralogy and to incorporate proton- and oxalate-promoted dissolution schemes for the iron-containing dust aerosols. The main focus of this work is on the factors influencing the deposition of soluble iron from dust in the Northwest Pacific during the spring seasons from 2001 to 2017, with evaluation against observational datasets from the North Pacific. The authors report a decrease in the deposition of soluble iron from East Asia to the Northwest Pacific during the studied period, which is attributed to reduced dust emissions. However, they also observe an increase in dust iron solubility, primarily linked to the atmospheric processing of coarse dust aerosols. Sensitivity simulations indicate that rising anthropogenic NOx emissions, rather than a reduction in SO2, are the primary factor influencing dust aerosol acidity in the model, leading overall to an increase in iron solubility despite the decrease in iron from dust. The manuscript is well-written; however, some issues concerning the methodology and the presentation of results should be addressed before final publication. This will help readers better understand the assumptions considered in this work along with the uncertainties surrounding the main conclusions derived from model simulations.
General comments
- The authors used the global model CAM6-chem to simulate the soluble iron deposition over the Northwest Pacific. Given that a number of global modelling studies provide global budget calculations of the atmospheric iron cycle (i.e., burdens, wet and dry deposition rates, iron solubilisation rates, etc.), both for total and soluble iron or per mode (fine and coarse) iron-containing aerosols, the authors should provide their global estimations along with those for the study area. I also propose to present the budget calculations in a separate table and present other modelling estimates for comparison.
- In Sect. 2, the calculation of oxalate concentrations in the model used for the ligand-promoted dissolution is not clearly explained. The authors employed the formula from Hamilton et al. (2019) to estimate atmospheric oxalate levels based on the modeled secondary organic carbon concentrations. However, Hamilton et al. (2019) established a maximum aqueous concentration threshold of 15 μmol L−1, derived from the estimations of Scanza et al. (2018). What threshold is applied here? Do the authors calculate with their model version similar secondary organic carbon concentrations as reported by Scanza et al. (2018) and Hamilton et al. (2019)? If it differs, what threshold was used? Additionally, how might this assumption affect the simulated oxalate concentrations?
- The authors note that the model accurately captures oxalate observations. However, in Sect. 3.3.3, only the simulated surface oxalate concentration patterns over EA are presented, with no evaluation of the modeled OXL concentrations against observations. As far as I understand, the authors only compare spatial patterns from other modeling studies. I suggest that the authors present an evaluation of their model using observations (both globally and with a special focus on the EA region), as done in the other studies referenced in the manuscript.
- It is unclear how ligand-promoted dissolution is limited under cloud conditions in the model. I would expect a more detailed discussion of the cloud parameters that influence oxalate production, such as liquid water content (LWC) and cloud cover, as well as how these factors are incorporated into the process of oxalate-promoted iron dissolution.
- How much are the globally averaged dust emissions in the model? How is the emitted iron distributed between the fine (Aitken and accumulation) and coarse modes at dust emissions? What is the simulated global mean percentage of iron in dust? Additionally, what is the initial Fe solubility in dust? It would be beneficial for the reader to present some values used in the model, preferably in a separate table, instead of simply referring to the original publications.
- The authors indicate that oxalate-promoted processing accounts for 25% of total soluble iron deposition from dust, approximately double that of proton-promoted processing. This finding is noteworthy, as it contradicts other studies suggesting an alternative perspective. For example, Ito and Shi (2016) reported that the proton-promoted dissolution scheme contributed the majority of soluble iron deposition to the ocean, while Myriokefalitakis et al. (2022) found that proton-promoted dissolution is the primary process for dust aerosols, whereas ligand-promoted dissolution is considered more significant for combustion aerosols (which are not addressed in this study). Could this outcome of the model indicate an underestimation of aerosol acidity or an overestimation of oxalate concentrations within the model? Is this result only attributable to coarse-mode dust? Could you please provide further elaboration on this finding?
- In Fig. S2b, a weak correlation is observed in the evaluation of pH. Could you please provide relevant statistics and discuss potential reasons for the misrepresentation of atmospheric acidity in the model? It is expected that fine particles are relatively more acidic due to nss-sulfate and other acidic compounds contributions, while the coarse mode, which includes sea salt and dust, is much less acidic. Can you provide figures of the calculated pH values for each aerosol mode of the model?
- As a final comment, while the paper focuses on the deposition of soluble iron from dust aerosols, the omission of pyrogenic iron complicates a fair comparison with atmospheric observations. Numerous recent studies underscore the importance of pyrogenic iron from downwind source regions similar to the one examined here. It is unclear why the authors did not also include pyrogenic iron emissions in their analysis, especially since other versions of the model did. Consequently, when evaluating a model against observational data, the authors should preferably select cases where iron-containing dust aerosols predominantly influence the measured concentrations (e.g., by utilizing back trajectories). However, it is not clear whether this approach was implemented in the current study. Could you please provide some clarification on this?
Technical corrections
- Lines 121-123: Please rephrase. It is not obvious what the authors mean.
- Lines 252-254: Can you please also provide actual rates rather than just percentages? How are these numbers compared to other studies?
- Section 3.2: Deposition rates could also be presented in a table.
- Table 1: In general, all emissions come from CMIP6 with MEIC for China. Does this information really need to be repeated in the table? Moreover, why are chlorine emissions not presented in the table?
- Figure 3: I propose to color-code only the seasons, not the 12 months of the year. It would probably make the figure less noisy.
- In the whole manuscript: Better to change “oxalate-ligand-promoted” to either oxalate- or ligand-. Usually oxalate is used as a proxy for all organic ligands.
- Line 343: The NCP abbreviation needs to be explained.
- Line 344: Please explain why HCl concentrations are increased in the model. How have precursor emissions changed?
- Lines 366-367: Can you please explain why aerosol water content is increased due to enhanced NOx? How much has the coarse nitrate changed during the studied period? Please also discuss this, taking into account the general comments.
References
Hamilton, D. S., Scanza, R. A., Feng, Y., Guinness, J., Kok, J. F., Li, L., Liu, X., Rathod, S. D., Wan, J. S., Wu, M., and Mahowald, N. M.: Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0), Geosci. Model Dev., 12, 3835–3862, https://doi.org/10.5194/gmd-12-3835-2019, 2019.
Ito, A. and Shi, Z.: Delivery of anthropogenic bioavailable iron from mineral dust and combustion aerosols to the ocean, Atmos. Chem. Phys., 16, 85–99, https://doi.org/10.5194/acp-16-85-2016, 2016.
Myriokefalitakis, S., Bergas-Massó, E., Gonçalves-Ageitos, M., Pérez García-Pando, C., van Noije, T., Le Sager, P., Ito, A., Athanasopoulou, E., Nenes, A., Kanakidou, M., Krol, M. C., and Gerasopoulos, E.: Multiphase processes in the EC-Earth model and their relevance to the atmospheric oxalate, sulfate, and iron cycles, Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, 2022.
Scanza, R. A., Hamilton, D. S., Perez Garcia-Pando, C., Buck, C., Baker, A., and Mahowald, N. M.: Atmospheric processing of iron in mineral and combustion aerosols: development of an intermediate-complexity mechanism suitable for Earth system models, Atmos. Chem. Phys., 18, 14175–14196, https://doi.org/10.5194/acp-18-14175-2018, 2018.
Citation: https://doi.org/10.5194/egusphere-2024-2293-RC1 -
AC1: 'Reply on RC1', Hanzheng Zhu, 07 Jan 2025
We greatly appreciate the referee for the helpful comments. Please find our responses in the enclosed file.
Citation: https://doi.org/10.5194/egusphere-2024-2293-AC1 - AC3: 'Reply on AC1', Hanzheng Zhu, 08 Jan 2025
-
RC2: 'Comment on egusphere-2024-2293', Akinori Ito, 27 Nov 2024
General comments
Model predictions of chemical composition in dust aerosols and its effect on iron solubility and ocean biogeochemistry are highly uncertain. The authors implemented a mineralogical map and atmospheric processing schemes of dust to a global atmospheric chemistry model. They evaluated the model results against observations of total and soluble iron concentrations. They conducted sensitivity experiments to assess the impact of recent shifts in dust emissions and chemical compositions on soluble iron deposition in the Northwest Pacific. Their results indicate a decreasing trend in dust soluble iron deposition from East Asia to the Northwest Pacific by 2.4% per year, primarily due to reduced dust emissions, which are mainly driven by declining surface winds over dust source regions. They show an increasing trend in dust iron solubility from 1.5% in 2001 to 1.7% in 2017. This increased iron solubility is associated with the acidification of coarse mode aerosols due to the increase in anthropogenic NOx emissions and in-cloud oxalate-ligand-promoted dissolution. The modeling exercises in this paper may help us to advance modeling dust iron. However, the differences of their model development from previous studies are unclear in its current form. It is more appropriate to cite parent papers rather than the following papers to clarify the model development. I have some comments and questions to improve this paper.
Specific comments
l.89: How did you calculate Na+ on dust?
l.98 and Fig. 7: What are the criteria using either (4a) or (4b)? Please elucidate how alkaline cations as in (3a) are considered for pH calculation in the sulfate-poor conditions to understand the acidic conditions in coarse particles over the oceans (see below comments on Fig. 7).
l.112: This mineralogy map has been implemented by previous studies. Please cite the parent papers rather than your references and elucidate the differences from previous studies.
l.114: Please specify iron content and initial iron solubility in minerals.
l.119: This scheme has been implemented by previous studies. Please cite the parent papers rather than your references and elucidate the differences from previous studies.
l.122, Figure S1: Please show the comparison of iron content with observations to prove the underestimations of dust iron in main dust source regions by default settings and discuss the major reasons.
l.124: The two atmospheric processing has been implemented by previous studies. Please cite the parent papers rather than your references and elucidate the differences from previous studies.
l.135, Figure S2: Did you use the annually averaged pH values? Please rephrase observed pH, because there is no direct measurement of pH in aerosol. Since this study focuses on coarse mode aerosols, why don’t you show the similar figure for the coarse mode to support the model performance?
l.147 and section 3.3.3: Please show the comparisons with observed oxalate levels in East Asia cloud water.
l. 215, Figure 3: Please show the comparison of iron solubility.
l.255: There is no reference of observational research to support this modelling results. Please correct the sentence or add the reference if any.
l.266, l.272 and Figure S5: It is not clear whether coarse-mode proton-promoted soluble iron deposition increased by 7% from Fig. 5. Please show the statistics for the increased trends. Please show the trend of total soluble iron deposition clearly to elucidate atmospheric processing contributed to the increase in dust iron solubility.
l.281: The decreasing trend of dust aerosol concentration over EA and a strong correlation between surface wind speed and dust emission has been shown in previous studies, as you cited. Please discuss the similarities and differences between this and previous studies quantitatively.
l.320 and Fig. 7: Since the first-order mass transfer coefficient is used to simulate size-dependent pH, HNO3 absorption in the coarse mode should occur slowly during the long-range transport. Thus, it is unclear why pH is suddenly dropped once the aerosols are transported over the oceans. Please specify the mechanisms of quickly elevated aerosol acidity over the oceans. It is also unclear how calcite in coarse particles is consumed. Please show the spatial distribution of excess Ca (in the form of CaCO3 in MOSAIC), CaSO4, Ca(NO3)2 and CaCl2 and specify the mechanisms of acidification of coarse particles.
l.334: Please compare with the dust aerosols, instead of sea spray aerosols cited in this sentence.
l.341, l.445: Please explain why HCl emissions and concentrations were increased?
l.370: The dominant role of NOx in the long-term trend of dust iron solubility has been shown in previous studies, as you cited. Please discuss the similarities and differences between this and previous studies quantitatively.
l.386: The oxalate-promoted dissolution of iron in acidic solution is much faster than cloud water conditions according to laboratory experiments. However, the oxalate-promoted dissolution of iron in aerosol is not considered in this study. Please specify the most rapid conditions.
Technical comments
l.85: Please indicate the unit of T.
l.90: Please indicate the unit of m.
l.95: Please indicate the unit of K. Please explain the activity coefficient used in (3b).
l.100: Please indicate the unit of C.
l.173, Figure S4, and Figure 3: Please specify GEOTRACES, cite the parent papers, and follow the Fair Use Agreement (Fair_Data_Use_Statement-for-IDP2021v2.pdf).
l.432: Please correct to Fig. 10 (c).
Citation: https://doi.org/10.5194/egusphere-2024-2293-RC2 - AC2: 'Reply on RC2', Hanzheng Zhu, 07 Jan 2025
Status: closed
-
RC1: 'Comment on egusphere-2024-2293', Anonymous Referee #2, 11 Nov 2024
Summary
This study examines the deposition of soluble iron from dust aerosols using the Community Atmosphere Model version 6 (CAM6-chem). CAM6-chem has been developed here to include desert dust mineralogy and to incorporate proton- and oxalate-promoted dissolution schemes for the iron-containing dust aerosols. The main focus of this work is on the factors influencing the deposition of soluble iron from dust in the Northwest Pacific during the spring seasons from 2001 to 2017, with evaluation against observational datasets from the North Pacific. The authors report a decrease in the deposition of soluble iron from East Asia to the Northwest Pacific during the studied period, which is attributed to reduced dust emissions. However, they also observe an increase in dust iron solubility, primarily linked to the atmospheric processing of coarse dust aerosols. Sensitivity simulations indicate that rising anthropogenic NOx emissions, rather than a reduction in SO2, are the primary factor influencing dust aerosol acidity in the model, leading overall to an increase in iron solubility despite the decrease in iron from dust. The manuscript is well-written; however, some issues concerning the methodology and the presentation of results should be addressed before final publication. This will help readers better understand the assumptions considered in this work along with the uncertainties surrounding the main conclusions derived from model simulations.
General comments
- The authors used the global model CAM6-chem to simulate the soluble iron deposition over the Northwest Pacific. Given that a number of global modelling studies provide global budget calculations of the atmospheric iron cycle (i.e., burdens, wet and dry deposition rates, iron solubilisation rates, etc.), both for total and soluble iron or per mode (fine and coarse) iron-containing aerosols, the authors should provide their global estimations along with those for the study area. I also propose to present the budget calculations in a separate table and present other modelling estimates for comparison.
- In Sect. 2, the calculation of oxalate concentrations in the model used for the ligand-promoted dissolution is not clearly explained. The authors employed the formula from Hamilton et al. (2019) to estimate atmospheric oxalate levels based on the modeled secondary organic carbon concentrations. However, Hamilton et al. (2019) established a maximum aqueous concentration threshold of 15 μmol L−1, derived from the estimations of Scanza et al. (2018). What threshold is applied here? Do the authors calculate with their model version similar secondary organic carbon concentrations as reported by Scanza et al. (2018) and Hamilton et al. (2019)? If it differs, what threshold was used? Additionally, how might this assumption affect the simulated oxalate concentrations?
- The authors note that the model accurately captures oxalate observations. However, in Sect. 3.3.3, only the simulated surface oxalate concentration patterns over EA are presented, with no evaluation of the modeled OXL concentrations against observations. As far as I understand, the authors only compare spatial patterns from other modeling studies. I suggest that the authors present an evaluation of their model using observations (both globally and with a special focus on the EA region), as done in the other studies referenced in the manuscript.
- It is unclear how ligand-promoted dissolution is limited under cloud conditions in the model. I would expect a more detailed discussion of the cloud parameters that influence oxalate production, such as liquid water content (LWC) and cloud cover, as well as how these factors are incorporated into the process of oxalate-promoted iron dissolution.
- How much are the globally averaged dust emissions in the model? How is the emitted iron distributed between the fine (Aitken and accumulation) and coarse modes at dust emissions? What is the simulated global mean percentage of iron in dust? Additionally, what is the initial Fe solubility in dust? It would be beneficial for the reader to present some values used in the model, preferably in a separate table, instead of simply referring to the original publications.
- The authors indicate that oxalate-promoted processing accounts for 25% of total soluble iron deposition from dust, approximately double that of proton-promoted processing. This finding is noteworthy, as it contradicts other studies suggesting an alternative perspective. For example, Ito and Shi (2016) reported that the proton-promoted dissolution scheme contributed the majority of soluble iron deposition to the ocean, while Myriokefalitakis et al. (2022) found that proton-promoted dissolution is the primary process for dust aerosols, whereas ligand-promoted dissolution is considered more significant for combustion aerosols (which are not addressed in this study). Could this outcome of the model indicate an underestimation of aerosol acidity or an overestimation of oxalate concentrations within the model? Is this result only attributable to coarse-mode dust? Could you please provide further elaboration on this finding?
- In Fig. S2b, a weak correlation is observed in the evaluation of pH. Could you please provide relevant statistics and discuss potential reasons for the misrepresentation of atmospheric acidity in the model? It is expected that fine particles are relatively more acidic due to nss-sulfate and other acidic compounds contributions, while the coarse mode, which includes sea salt and dust, is much less acidic. Can you provide figures of the calculated pH values for each aerosol mode of the model?
- As a final comment, while the paper focuses on the deposition of soluble iron from dust aerosols, the omission of pyrogenic iron complicates a fair comparison with atmospheric observations. Numerous recent studies underscore the importance of pyrogenic iron from downwind source regions similar to the one examined here. It is unclear why the authors did not also include pyrogenic iron emissions in their analysis, especially since other versions of the model did. Consequently, when evaluating a model against observational data, the authors should preferably select cases where iron-containing dust aerosols predominantly influence the measured concentrations (e.g., by utilizing back trajectories). However, it is not clear whether this approach was implemented in the current study. Could you please provide some clarification on this?
Technical corrections
- Lines 121-123: Please rephrase. It is not obvious what the authors mean.
- Lines 252-254: Can you please also provide actual rates rather than just percentages? How are these numbers compared to other studies?
- Section 3.2: Deposition rates could also be presented in a table.
- Table 1: In general, all emissions come from CMIP6 with MEIC for China. Does this information really need to be repeated in the table? Moreover, why are chlorine emissions not presented in the table?
- Figure 3: I propose to color-code only the seasons, not the 12 months of the year. It would probably make the figure less noisy.
- In the whole manuscript: Better to change “oxalate-ligand-promoted” to either oxalate- or ligand-. Usually oxalate is used as a proxy for all organic ligands.
- Line 343: The NCP abbreviation needs to be explained.
- Line 344: Please explain why HCl concentrations are increased in the model. How have precursor emissions changed?
- Lines 366-367: Can you please explain why aerosol water content is increased due to enhanced NOx? How much has the coarse nitrate changed during the studied period? Please also discuss this, taking into account the general comments.
References
Hamilton, D. S., Scanza, R. A., Feng, Y., Guinness, J., Kok, J. F., Li, L., Liu, X., Rathod, S. D., Wan, J. S., Wu, M., and Mahowald, N. M.: Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0), Geosci. Model Dev., 12, 3835–3862, https://doi.org/10.5194/gmd-12-3835-2019, 2019.
Ito, A. and Shi, Z.: Delivery of anthropogenic bioavailable iron from mineral dust and combustion aerosols to the ocean, Atmos. Chem. Phys., 16, 85–99, https://doi.org/10.5194/acp-16-85-2016, 2016.
Myriokefalitakis, S., Bergas-Massó, E., Gonçalves-Ageitos, M., Pérez García-Pando, C., van Noije, T., Le Sager, P., Ito, A., Athanasopoulou, E., Nenes, A., Kanakidou, M., Krol, M. C., and Gerasopoulos, E.: Multiphase processes in the EC-Earth model and their relevance to the atmospheric oxalate, sulfate, and iron cycles, Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, 2022.
Scanza, R. A., Hamilton, D. S., Perez Garcia-Pando, C., Buck, C., Baker, A., and Mahowald, N. M.: Atmospheric processing of iron in mineral and combustion aerosols: development of an intermediate-complexity mechanism suitable for Earth system models, Atmos. Chem. Phys., 18, 14175–14196, https://doi.org/10.5194/acp-18-14175-2018, 2018.
Citation: https://doi.org/10.5194/egusphere-2024-2293-RC1 -
AC1: 'Reply on RC1', Hanzheng Zhu, 07 Jan 2025
We greatly appreciate the referee for the helpful comments. Please find our responses in the enclosed file.
Citation: https://doi.org/10.5194/egusphere-2024-2293-AC1 - AC3: 'Reply on AC1', Hanzheng Zhu, 08 Jan 2025
-
RC2: 'Comment on egusphere-2024-2293', Akinori Ito, 27 Nov 2024
General comments
Model predictions of chemical composition in dust aerosols and its effect on iron solubility and ocean biogeochemistry are highly uncertain. The authors implemented a mineralogical map and atmospheric processing schemes of dust to a global atmospheric chemistry model. They evaluated the model results against observations of total and soluble iron concentrations. They conducted sensitivity experiments to assess the impact of recent shifts in dust emissions and chemical compositions on soluble iron deposition in the Northwest Pacific. Their results indicate a decreasing trend in dust soluble iron deposition from East Asia to the Northwest Pacific by 2.4% per year, primarily due to reduced dust emissions, which are mainly driven by declining surface winds over dust source regions. They show an increasing trend in dust iron solubility from 1.5% in 2001 to 1.7% in 2017. This increased iron solubility is associated with the acidification of coarse mode aerosols due to the increase in anthropogenic NOx emissions and in-cloud oxalate-ligand-promoted dissolution. The modeling exercises in this paper may help us to advance modeling dust iron. However, the differences of their model development from previous studies are unclear in its current form. It is more appropriate to cite parent papers rather than the following papers to clarify the model development. I have some comments and questions to improve this paper.
Specific comments
l.89: How did you calculate Na+ on dust?
l.98 and Fig. 7: What are the criteria using either (4a) or (4b)? Please elucidate how alkaline cations as in (3a) are considered for pH calculation in the sulfate-poor conditions to understand the acidic conditions in coarse particles over the oceans (see below comments on Fig. 7).
l.112: This mineralogy map has been implemented by previous studies. Please cite the parent papers rather than your references and elucidate the differences from previous studies.
l.114: Please specify iron content and initial iron solubility in minerals.
l.119: This scheme has been implemented by previous studies. Please cite the parent papers rather than your references and elucidate the differences from previous studies.
l.122, Figure S1: Please show the comparison of iron content with observations to prove the underestimations of dust iron in main dust source regions by default settings and discuss the major reasons.
l.124: The two atmospheric processing has been implemented by previous studies. Please cite the parent papers rather than your references and elucidate the differences from previous studies.
l.135, Figure S2: Did you use the annually averaged pH values? Please rephrase observed pH, because there is no direct measurement of pH in aerosol. Since this study focuses on coarse mode aerosols, why don’t you show the similar figure for the coarse mode to support the model performance?
l.147 and section 3.3.3: Please show the comparisons with observed oxalate levels in East Asia cloud water.
l. 215, Figure 3: Please show the comparison of iron solubility.
l.255: There is no reference of observational research to support this modelling results. Please correct the sentence or add the reference if any.
l.266, l.272 and Figure S5: It is not clear whether coarse-mode proton-promoted soluble iron deposition increased by 7% from Fig. 5. Please show the statistics for the increased trends. Please show the trend of total soluble iron deposition clearly to elucidate atmospheric processing contributed to the increase in dust iron solubility.
l.281: The decreasing trend of dust aerosol concentration over EA and a strong correlation between surface wind speed and dust emission has been shown in previous studies, as you cited. Please discuss the similarities and differences between this and previous studies quantitatively.
l.320 and Fig. 7: Since the first-order mass transfer coefficient is used to simulate size-dependent pH, HNO3 absorption in the coarse mode should occur slowly during the long-range transport. Thus, it is unclear why pH is suddenly dropped once the aerosols are transported over the oceans. Please specify the mechanisms of quickly elevated aerosol acidity over the oceans. It is also unclear how calcite in coarse particles is consumed. Please show the spatial distribution of excess Ca (in the form of CaCO3 in MOSAIC), CaSO4, Ca(NO3)2 and CaCl2 and specify the mechanisms of acidification of coarse particles.
l.334: Please compare with the dust aerosols, instead of sea spray aerosols cited in this sentence.
l.341, l.445: Please explain why HCl emissions and concentrations were increased?
l.370: The dominant role of NOx in the long-term trend of dust iron solubility has been shown in previous studies, as you cited. Please discuss the similarities and differences between this and previous studies quantitatively.
l.386: The oxalate-promoted dissolution of iron in acidic solution is much faster than cloud water conditions according to laboratory experiments. However, the oxalate-promoted dissolution of iron in aerosol is not considered in this study. Please specify the most rapid conditions.
Technical comments
l.85: Please indicate the unit of T.
l.90: Please indicate the unit of m.
l.95: Please indicate the unit of K. Please explain the activity coefficient used in (3b).
l.100: Please indicate the unit of C.
l.173, Figure S4, and Figure 3: Please specify GEOTRACES, cite the parent papers, and follow the Fair Use Agreement (Fair_Data_Use_Statement-for-IDP2021v2.pdf).
l.432: Please correct to Fig. 10 (c).
Citation: https://doi.org/10.5194/egusphere-2024-2293-RC2 - AC2: 'Reply on RC2', Hanzheng Zhu, 07 Jan 2025
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 331 | 88 | 38 | 457 | 56 | 22 | 21 |
- HTML: 331
- PDF: 88
- XML: 38
- Total: 457
- Supplement: 56
- BibTeX: 22
- EndNote: 21
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 149 | 34 |
| China | 2 | 118 | 26 |
| Japan | 3 | 17 | 3 |
| United Kingdom | 4 | 15 | 3 |
| Russia | 5 | 14 | 3 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 149