the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jun 2025
Modeling PFAS in the global atmosphere – The PRIEST extension for the ICON-ART modeling framework
Abstract. This study presents the ICON-ART PRIEST model extension, developed to simulate the transport and transformation of Per- and Polyfluorinated Substances (PFAS) in the atmosphere. While the ICON-ART framework was developed to simulate atmospheric physics and chemical composition, the newly developed PRIEST extension incorporates additional gas‐phase and aqueous physics, along with chemical reaction mechanisms, to model the transport, transformation, and deposition of PFCA precursors. Therefore, the model includes 22 aqueous-phase reactions that depend on liquid cloud water and temperature. The aqueous-phase processes represent the adsorption of precursors in water droplets, with variable absorption rates. The model follows a hierarchical initialization, starting with the emissions, followed by aerosols, chemistry, and finally removal. A simple parameterization of the OH radical is implemented to improve the simulation of PFCA precursors. The global model results (approx 105 km² grid resolution and 6 hours temporal resolution) show the capability of the model system to simulate regional and global variations of PFCA concentrations and their deposition processes. The results reveal an overestimation of observed atmospheric concentrations in Europe and an underestimation in East Asia. These differences are mainly related to the coarse spatial model resolution and the uncertainties arising from the underlying emissions model. In conclusion, ICON-ART PRIEST represents a significant step forward in simulating the atmospheric fate of PFCAs precursors and their transformation products by integrating an enhanced chemical mechanism into the ICON-ART framework that couples both gas-phase and aqueous-phase processes, and also with the incorporation of a detailed temporally resolved PFAS emission inventory.
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CEC1: 'Comment on egusphere-2025-2289', Juan Antonio Añel, 24 Jul 2025
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AC1: 'Reply on CEC1', Hiram Abif Meza Landero, 14 Nov 2025
Dear Dr Añel,
thank you very much for your message and for pointing out these issues with our manuscript and its compliance with the *Code and Data Policy*. We apologise for the inconvenience caused.
Regarding the model code, we have now archived ICON-ART in Zenodo. Due to specific restrictions associated with the ICON-ART licence, which is not fully public, the code is currently deposited in a private Zenodo record. The corresponding DOI is: https://doi.org/10.5281/zenodo.16615114
Concerning the model output, the full raw data volume is approximately 72 TB, which makes it impractical to store entirely in a public repository. However, all data needed to reproduce the figures and analysis in the paper have been prepared and deposited as follows:
- Monthly model output used in the analysis: https://doi.org/10.5281/zenodo.16740291
- Model output interpolated to measurement sites: https://doi.org/10.5281/zenodo.16795424In addition, the input data sets used in the simulations are now also archived:
- POPE emission input data interpolated to the ICON-ART PRIEST grid: https://doi.org/10.5281/zenodo.16539566
- CAM-chem background concentrations interpolated to the ICON-ART PRIEST grid: https://doi.org/10.5281/zenodo.16510044We will update the *Code and Data Availability* section in the revised manuscript to include all these DOIs and a clear description of the contents of each repository, in accordance with the journal’s policy.
Thank you again for your comment in order to improve the quality and reproducibility of our current research.
With best regards,
Hiram Meza
on behalf of all co-authors
Citation: https://doi.org/10.5194/egusphere-2025-2289-AC1
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AC1: 'Reply on CEC1', Hiram Abif Meza Landero, 14 Nov 2025
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RC1: 'Comment on egusphere-2025-2289', Anonymous Referee #1, 19 Aug 2025
General Comments
Meza-Landero et al. reported a newly developed atmospheric PFAS modeling framework, ICON-ART (ICOsahedral Non-hydrostatic model framework with Aerosol and Reactive Tracers) PRIEST (PFCA Reactions In Earth System Transport). They described the implementation of multiphase chemistry, transport, and deposition of PFCA (Perfluoroalkyl Carboxylic Acids) in the model, and evaluated surface concentrations and wet deposition against in-situ observations. The validation demonstrated that the model could reasonably capture the wet deposition of PFOA (Perfluorooctanoic Acid) and PFNA (Perfluorononanoic Acid) compared with available observations. However, the model’s performance on surface concentrations of PFOA, PFNA, and FTOH (Fluorotelomer Alcohol) varied across regions. The research topic is novel and significant, since PFAS has been drawing attention as a new class of pollutants that raise public health concerns. The manuscript is suitable for publication in GMD. I have two major concerns that should be addressed before I can recommend it for publication.
- The setup of the model is quite concerning. The model reads the monthly output of NOx, OH, HO₂, and H₂O from CAM-chem, while organic peroxy radicals (RO₂) were calculated from the most abundant VOCs, including methane, ethane, and propane. First, CAM-chem was driven by its dynamic core, and it might be nudged by other reanalyses such as MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2). However, the meteorological fields in the authors’ model were simulated by its own dynamic module, ICON. There is a mismatch between the meteorological fields driving the two models, which makes it unreasonable to directly match the chemical fields from CAM-chem to the ICON meteorology fields. Second, NOx, HO₂, OH, RO₂, and VOCs are chemically linked. The way the authors calculated RO₂ from methane, ethane, and propane is physically unreasonable, as it breaks the mass conservation between these species. Overall, the authors’ current approach of combining meteorology and chemical fields violates fundamental physical laws, including conservation of momentum and mass. The authors should adopt either a fully online or a fully offline approach, rather than the hybrid method they use now.
- Further analysis is required to better understand the model performance. Figures D1 and D2 demonstrate that the model cannot capture the variability of surface concentrations of PFNA and PFOA, and the simulation results appear overly consistent. I think this is reasonable, as it could represent an initial step for this type of modeling. The coarse resolution could be a potential explanation for the discrepancy, however, I believe that more effort is needed to identify the sources of uncertainty. For example, the meteorological fields should be examined before validating PFCA. In addition, emissions could also be a potential source of uncertainty, and sensitivity tests would be necessary to assess their contribution. All these analyses would be helpful for both the authors and the readers in understanding the underlying issues with the model system.
Specific Comments
Abstract: “ICON-ART PRIEST” should be written out in full the first time it appears.
Lines 60–73: This paragraph is confusing. It is unclear whether it refers to updates or to the previous version of the ICON-ART PRIEST model. The authors seem to describe ICON-ART PRIEST as a new model, but placing this paragraph here is confusing. The updates should instead be mentioned after line 76.
Section 2.2.1: As I mentioned in my major comments, this monthly OH estimation may be inappropriate, given the connections between OH and other species.
Line 240: The data link is not available.
Figure 6: Error bars are necessary to better understand the zonal variability.
Citation: https://doi.org/10.5194/egusphere-2025-2289-RC1 -
AC2: 'Reply on RC1', Hiram Abif Meza Landero, 14 Nov 2025
Dear Referee 1,
Thank you very much for your thorough and constructive review, and for recognizing the novelty and relevance of our work and the suitability of the manuscript for GMD. We have carefully considered all your comments and have revised the manuscript accordingly. Below we respond point by point.
Major comment 1: Model setup, conservation laws, and RO₂ treatment.
You raised two closely related concerns: (i) the combination of CAM-chem chemical fields with ICON meteorology and the alleged violation of conservation laws, and (ii) the physically inconsistent treatment of RO₂.
(a) Use of CAM-chem fields and conservation of momentum/mass
We apologize that the original text did not clearly convey how the CAM-chem output is used in PRIEST. In the present version of ICON-ART PRIEST:
NOx, OH, HO₂, H₂O, RO₂ and VOCs from CAM-chem are not transported prognostic tracers in ICON.
They enter the PFAS chemistry only as prescribed background fields to compute reaction rates. The only substances that are actually transported and deposited by ICON-ART PRIEST are the PFCA tracers (PFOA, PFNA, etc.). The CAM-chem-derived species have no associated continuity or momentum equation in ICON, so there is no second “dynamical core” transporting them.Because of (1), there is no overlapping of dynamical cores.
ICON’s dynamical core transport only the meteorological state and the transported PFAS tracers. The CAM-chem fields are used diagnostically as space–time snapshots (monthly means), and do not evolve dynamically within ICON. Therefore, the conservation equations solved by ICON are not altered or duplicated, and momentum conservation is not violated.The CAM-chem fields enter as monthly means, which already filter out sub-monthly dynamical variability.
The original CAM-chem fields are 6-hourly and fully consistent with their own dynamics, but by the time they are monthly averaged, much of the dynamical information associated with advective transport and transient perturbations is removed. The ICON model thus treat them as slowly varying background fields rather than as dynamically active tracers. In this sense, they behave like climatological or reanalysis-based oxidant fields often used in offline chemistry calculations.This type of semi-offline coupling is standard practice in atmospheric modeling.
Regional and global chemistry models (e.g. limited-area models driven by global CTMs) routinely use oxidants or other species from external models as time-varying boundary/initial or background fields without being considered as overlapping dynamical cores. We have now made this point more explicit in the revised text to avoid the impression that two independent dynamical systems are transporting the same tracers.To make all this transparent, we have explicitly mentioned it in the description of the chemical setup in Sect. 2.2.1. We now explicitly state that:
NOx, OH, HO₂, H₂O, RO₂, and VOCs from CAM-chem are used as prescribed monthly fields.
Only PFAS species are transported by ICON-ART PRIEST.
The CAM-chem fields have no momentum or transport equations in ICON and thus do not affect momentum conservation.
We hope this clarifies that no fundamental physical conservation law is violated by our current setup.
(b) RO₂ calculation and mass balance
Regarding the second part of your first major comment (RO₂ calculation), we agree that OH, NOx, HO₂, RO₂ and VOCs are chemically coupled in reality. In the current PRIEST version:
CH₄, C₂H₆, and C₃H₈, as well as OH, HO₂ and NOx, from CAM-chem are used as background concentrations to diagnose RO₂.
These background species are not transported or chemically updated within ICON. Hence, as background fields, they do not directly affect the internal mass conservation of transported PFAS tracers in ICON-ART PRIEST.
Our initial reasoning was that, since the VOCs and oxidants are not prognostic in ICON and only the PFAS species are transported and deposited, the global mass balance of PFAS would not be compromised by the approximate RO₂ calculation. However, we agree with you that from a chemical-mechanism point of view, using a simplified RO₂ diagnostic based on a subset of VOCs and prescribed OH/HO₂/NOx is not ideal and breaks, at least conceptually, the strict chemical linkage among these species.
To address this concern, we have:
Clarified in the revised manuscript that RO₂, OH, HO₂ and NOx are treated as prescribed background fields in the current PRIEST version, and we openly discuss the associated limitation for chemical consistency (Sect. 2.2.1 and Sect. 4).
Reduced the risk of internal inconsistencies in the PFAS chemistry.
We have revisited the implementation of the reaction mechanism to ensure that the conversion rates involving OH, HO₂ and NOx in the PFAS pathway are fully consistent with the prescribed CAM-chem fields used in the reactions, to avoid any hidden mass imbalance affecting PFAS yields.Outlined a concrete plan for improving RO₂ in a future PRIEST release.
We now explicitly state that RO₂ will be calculated interactively within an extended chemical mechanism in a future version of ICON-ART PRIEST, so that RO₂ production and loss are treated consistently with the VOC and oxidant fields. This will remove the current diagnostic approximation and ensure a fully self-consistent photochemical system.We also note, as pointed out by the additional reviewer (published by our editor, Dr. Hoffmann), that while the semi-offline treatment of non-PFAS reactants introduces errors due to the mismatch between CAM-chem chemistry and ICON dynamics, these errors are expected to be significantly smaller than the PFAS-specific uncertainties at this stage. We agree with this assessment and have strengthened the uncertainty and sensitivity analysis accordingly.
Major comment 2: Further analysis of model performance, sensitivity, and uncertainties.
We fully agree that a more systematic assessment of uncertainties and sensitivities is important at this early stage of PFAS modeling. Following your suggestion, we conducted a new suite of eleven additional simulations (each 2 years long, with the first year used as spin-up) designed to separate the influence of emissions, oxidant fields, and internal variability:
Group 1 (Control + oxidant sensitivity):
1 Control experiment using the POPE “Best Guess” emission scenario.
4 oxidant sensitivity experiments, where OH and RO₂ concentrations are scaled (0× and 10×) to explore the sensitivity of PFCA formation to these reactants.
Group 2 (emission uncertainty and initialization):
6 emission-uncertainty experiments, using the Upper Bound (UB) and Lower Bound (LB) POPE emission scenarios, each initialized at three different times (one month before, at, and one month after 2006-01-01) to sample internal variability associated with different initial conditions.
From these experiments we performed the following analyses (now summarized in a new Appendix E):
Relative mean differences of annual mean PFOA and PFNA columns and deposition for both groups relative to the Control, and between the different initialization dates (Appendix Tables E1–E3 and Figs. E1–E4).
This analysis shows that POPE emission uncertainties (UB vs. LB) lead to substantially larger changes in PFOA/PFNA than the differences caused by varying the initialization date, indicating that emission uncertainty is a primary driver of PFAS spread in our system.
ANOVA and paired permutation tests:
An ANOVA was conducted to test differences in the means between Group 2 experiments (UB/LB) and the Control.
Additionally, because the experiments share a common emission origin within each scenario, we applied a paired permutation test to assess the significance of differences in spatial patterns.
The tables with these statistical results are now included as Tables E1–E3 in the revised manuscript.
Linearity analysis for oxidant sensitivity (Group 1):
We examined whether PFOA/PFNA production scales linearly with available OH and RO₂, or whether competing loss pathways saturate the PFAS yield.
The analysis shows that PFOA production is strongly and approximately linearly sensitive to OH, while PFNA exhibits a much weaker response to OH changes.
PFNA also shows only a very small sensitivity to RO₂ variations, suggesting that for increased RO₂ concentrations, other chemical pathways (e.g. RO₂/OH + NO, RO₂/OH + RO₂) consume RO₂ without producing PFCA, leading to a saturation effect.
The corresponding diagnostics are now presented in Fig. E5.
Signal-to-Noise ratio (SNR) analysis:
To quantify the relative importance of emissions (“signal”) versus internal variability from different initialization times (“noise”), we computed the Signal-to-Noise ratio for Group 2.
The resulting maps (Fig. E6) show a strong signal, i.e. emissions clearly dominate over internal variability in determining the simulated PFCA fields. This supports the interpretation that uncertainties in emissions are a key limitation of current PFAS modeling, consistent with your comment and Dr. Hoffmann’s assessment.
These new analyses directly respond to your request to better identify the main sources of uncertainty and to clarify whether discrepancies in surface concentrations and deposition patterns are dominated by model physics, oxidant fields, or emissions. We have added a new subsection in the Discussion summarizing these findings and explicitly stating that, at this stage, uncertainties in emissions and in oxidant fields are larger than those introduced by the semi-offline coupling.
### Major comment 2 – Further analysis of model performance, sensitivity, and uncertainties
We fully agree that a more systematic assessment of uncertainties and sensitivities is important at this early stage of PFAS modeling. Following your suggestion, we conducted a new suite of **eleven additional simulations** (each 2 years long, with the first year used as spin-up) designed to separate the influence of emissions, oxidant fields, and internal variability:
- **Group 1 (Control + oxidant sensitivity):**
- 1 **Control experiment** using the POPE “Best Guess” emission scenario.
- 4 **oxidant sensitivity experiments**, where OH and RO₂ concentrations are scaled (0× and 10×) to explore the sensitivity of PFCA formation to these reactants.
- **Group 2 (emission uncertainty and initialization):**
- 6 **emission-uncertainty experiments**, using the Upper Bound (UB) and Lower Bound (LB) POPE emission scenarios, each initialized at three different times (one month before, at, and one month after 2006-01-01) to sample internal variability associated with different initial conditions.From these experiments we performed the following analyses (now summarized in a new Appendix E):
1. **Relative mean differences** of annual mean PFOA and PFNA columns and deposition for both groups relative to the Control, and between the different initialization dates (Appendix Tables E1–E3 and Figs. E1–E4).
- This analysis shows that POPE emission uncertainties (UB vs. LB) lead to substantially larger changes in PFOA/PFNA than the differences caused by varying the initialization date, indicating that **emission uncertainty is a primary driver** of PFAS spread in our system.
2. **ANOVA and paired permutation tests**:
- An **ANOVA** was conducted to test differences in the means between Group 2 experiments (UB/LB) and the Control.
- Additionally, because the experiments share a common emission origin within each scenario, we applied a **paired permutation test** to assess the significance of differences in spatial patterns.
- The tables with these statistical results are now included as **Tables E1–E3** in the revised manuscript.
3. **Linearity analysis for oxidant sensitivity (Group 1):**
- We examined whether PFOA/PFNA production scales linearly with available OH and RO₂, or whether competing loss pathways saturate the PFAS yield.
- The analysis shows that **PFOA production is strongly and approximately linearly sensitive to OH**, while PFNA exhibits a much weaker response to OH changes.
- PFNA also shows only a very small sensitivity to RO₂ variations, suggesting that for increased RO₂ concentrations, other chemical pathways (e.g. RO₂/OH + NO, RO₂/OH + RO₂) consume RO₂ without producing PFCA, leading to a **saturation effect**.
- The corresponding diagnostics are now presented in **Fig. E5**.
4. **Signal-to-Noise ratio (SNR) analysis:**
- To quantify the relative importance of emissions (“signal”) versus internal variability from different initialization times (“noise”), we computed the **Signal-to-Noise ratio** for Group 2.
- The resulting maps (Fig. E6) show a **strong signal**, i.e. emissions clearly dominate over internal variability in determining the simulated PFCA fields. This supports the interpretation that uncertainties in emissions are a key limitation of current PFAS modeling, consistent with your comment and Dr. Hoffmann’s assessment.These new analyses directly respond to your request to better identify the main sources of uncertainty and to clarify whether discrepancies in surface concentrations and deposition patterns are dominated by model physics, oxidant fields, or emissions. We have added a new subsection in the Discussion summarizing these findings and explicitly stating that, at this stage, uncertainties in emissions and in oxidant fields are larger than those introduced by the semi-offline coupling.
The specific comments were addressed into the updated manuscript.
Once again, we thank you sincerely for your insightful comments. They have helped us to clarify the physical consistency of our setup, to better articulate the limitations of the current semi-offline approach, and to substantially strengthen the uncertainty and sensitivity analysis in the revised manuscript.
Kind regards,Hiram Meza
Citation: https://doi.org/10.5194/egusphere-2025-2289-AC2
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RC2: 'Comment on egusphere-2025-2289', Anonymous Referee #2, 10 Sep 2025
I find this a very challenging manuscript to review. Understanding global distributions of PFAS and associated atmospheric chemistry is an important problem and the authors present a significant advancement in the coupling of the simplified PFAS chemistry to the ICON model. On the other hand, it is apparent from Figures D1 and D2 that the model is not capable of reproducing the observed spatial variability of PFAO and PFNA. Given the simplified mechanism, uncertainties in global emission inventories, potential mismatches between chemical fields introduced by the use of the CAM-chem model for initialization of atmospheric oxidants, as well as limited observations for model validation, it is unclear what causes these mismatches and what steps could be undertaken to improve simulations. Given that this is a model development paper, I am not sure how to properly weigh these against each other or to suggest a more in-depth study to investigate the sources of uncertainty in model results.
However, specifically there are some suggestions outlined in specific comments
- In line with Reviewer 1's comment, the authors should explore ways to minimize the identified mismatch
- Whenever possible Figures should communicate uncertainties/ variation in the data beyond bar-plots (especially if n=2, which would merit just plotting point data). For higher n, boxplots or distribution plots would be appropriate- Figure 2: the different scales with change in color in Figure 2 are confusing and should be highlighted in caption (or otherwise changed)
- Code availability: It is inadvisable to solely rely on an institutional Git repository. The release should be tagged and submitted to an archive, such as zenodo to ensure preservation.
Citation: https://doi.org/10.5194/egusphere-2025-2289-RC2 -
AC3: 'Reply on RC2', Hiram Abif Meza Landero, 14 Nov 2025
Dear Referee 2,
Thank you very much for your thoughtful and honest assessment of our work, and for recognizing both the importance of the PFAS problem and the challenge of evaluating a model-development paper at this early stage.
Regarding the mismatch between simulated and observed spatial variability of PFOA and PFNA, we now make much clearer that several factors contribute: (i) large uncertainties in global PFAS emissions, (ii) the simplified PFAS mechanism, (iii) the semi-offline use of CAM-chem oxidant fields, and (iv) limited observations. To address this, we have added a new set of short ensemble simulations in which we vary POPE emissions (Upper/Lower Bound and Best Guess), oxidant fields (scaling OH and RO₂), and initialization dates. From these, we compute relative differences, ANOVA and permutation tests, and a Signal-to-Noise ratio. The results (now summarized in a new appendix) show that uncertainties in emissions and oxidants clearly dominate over internal model variability, which directly answers your concern about what drives the mismatches and where improvements should focus.
In line with Reviewer 1’s comment, we also clarify the treatment of CAM-chem oxidants: NOx, OH, HO₂, RO₂, H₂O and VOCs from CAM-chem are used as prescribed monthly-mean background fields only; they are not transported by ICON, and only PFAS species are prognostic tracers. This avoids overlapping dynamical cores and ensures that conservation in ICON applies to the transported PFAS tracers. At the same time, we explicitly discuss the limitations of this semi-offline setup and outline a planned future version with a more interactive treatment of RO₂ and oxidant chemistry.
Concerning your specific comments:
We have addressed each of your specific comments in the updated manuscript.
We are grateful for your careful reading and suggestions, which have helped us sharpen the discussion of uncertainties and make the model’s limitations and next steps much more transparent.
Warm regards,Hiram Meza
on behalf of all co-authors
Citation: https://doi.org/10.5194/egusphere-2025-2289-AC3
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AC3: 'Reply on RC2', Hiram Abif Meza Landero, 14 Nov 2025
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EC1: 'Comment on egusphere-2025-2289', Lars Hoffmann, 16 Sep 2025
Dear Authors,
We have received feedback from a third reviewer of your paper, which was not available before the interactive discussion closed, unfortunately.
Please take these comments into account when revising your manuscript.
Thank you and best regards
Lars HoffmannAdditional Reviewer Comments- Overall, I think this work is an important step for atmospheric PFAS modeling.
- Explicit inclusion of aqueous/aerosol chemistry is an important advancement for PFAS specifically. Multiple aerosol modes with different behaviors is likely a reasonable representation, though there is little literature constraint on the PFAS side of this process.
- While I agree with Referee 1 that the treatment of non-PFAS reactants as semi-offline provided from a different model will introduce errors based on the mismatch of dynamics and chemistry, I believe these errors will be significantly smaller than the PFAS-specific uncertainties of the model. This echoes the point of both Referee 1 and Referee 2 that uncertainties and sensitivities are an important aspect of this stage of modeling and should be explored.
- A striking difference between the model and observations (e.g. in Table 1) is in the degree of variability. The model is significantly less variable at a given site. Exploring how much of this can be explained by the use of monthly non-PFAS reactant concentrations, possible unresolved time/space-variability of emissions, or other reasons would be informative.
- I wonder if the PFCA model concentrations compared to observations are the total gas+particle phase? My inference is that they must be, but I think it's unclear in the manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-2289-EC1 -
AC4: 'Reply on EC1', Hiram Abif Meza Landero, 15 Nov 2025
Dear Dr. Hoffmann and Additional Reviewer,
Thank you very much for your careful evaluation and for recognizing the importance of explicit aqueous/aerosol chemistry and multi-mode aerosol treatment for PFAS modeling. Your comments align very well with those of Referees 1 and 2 and have helped us sharpen both the discussion and the analysis.
Semi-offline treatment of non-PFAS reactants We agree with your assessment that using semi-offline non-PFAS reactants (from CAM-chem) introduces inconsistencies between dynamics and chemistry, but that these are likely smaller than the PFAS-specific uncertainties at this stage. In the revised manuscript we clarify that:
NOx, OH, HO₂, RO₂, H₂O and VOCs from CAM-chem are used as prescribed monthly background fields only.
Only PFAS species are prognostic tracers transported by ICON-ART PRIEST; CAM-chem fields are not transported and have no momentum equation in ICON.
We also explicitly discuss this semi-offline choice as a current limitation and outline that a more interactive oxidant/RO₂ treatment is planned for future versions.
Uncertainties, sensitivities and variability In response to your and the other reviewers’ requests, we have added a dedicated uncertainty and sensitivity analysis based on eleven additional simulations (short 2-year runs, first year as spin-up):
Varying POPE emissions (Best Guess, Upper Bound, Lower Bound) and initialization dates.
Scaling OH and RO₂ to assess the sensitivity of PFOA/PFNA production to oxidant fields.
From these experiments we analyze relative differences, apply ANOVA and permutation tests, and compute a Signal-to-Noise ratio. The results (now summarized in a new appendix) show that:
Emission and oxidant uncertainties clearly dominate over internal variability,
PFOA is strongly sensitive to OH, while PFNA is comparatively less sensitive and shows signs of saturation with respect to RO₂ (other RO₂ loss pathways becoming more important).
Regarding the reduced variability at a given site (Table 1), we now explicitly discuss that this is consistent with:
- the use of monthly oxidant fields,
- the model resolution, and
- unresolved sub-grid variability in emissions. We have added text to make this interpretation clear and to emphasize that resolving finer time/space variability in emissions and oxidants is an important avenue for future work.
Phase state of PFCA in model–observation comparisons You are correct in your inference: the PFCA concentrations used for comparison with observations are total (gas + particle) phase. We now state this explicitly in the manuscript where the comparison is introduced.
We are grateful for your positive assessment and for the focused suggestions on uncertainties and variability, which have led to substantial improvements in the revised version.
Kind regards,
Hiram Meza
on behalf of all co-authors
Citation: https://doi.org/10.5194/egusphere-2025-2289-AC4
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
First, you have archived your code on a Git site. However, Git sites are not a suitable repositories for scientific publication; our policy is clear about it. Despite it, we have tried to access the code, and the link that you provide is empty, when trying to access the code through it, the message obtained is "page not found".
Also, we can not accept that the model output data is available upon request. You must share all the input files and output files used and produced in your work in a repository acceptable according to our policy.
Therefore, the current situation with your manuscript is irregular, as we can not accept in Discussions or send out for review manuscripts that do not comply with our policy. Please, publish your code and data in one of the appropriate repositories listed in our policy and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible. Also, you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the information of the new repositories.
I must note that if you do not fix this problem, we will not be able of proceeding with the review of your manuscript and publication in our journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor