the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Nov 2023
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
Abstract. Aqueous-phase chemical processes in clouds, fog, and deliquescent aerosols are known to alter atmospheric composition and acidity significantly. Traditionally, global and regional models predict aerosol composition by relying on thermodynamic equilibrium models and neglect non-equilibrium processes. Here, we present the AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel developed for the Modular Earth Submodel System (MESSy) as an add-on to the thermodynamic equilibrium model (i.e., ISORROPIA-II) used by MESSy’s Global Modal-aerosol eXtension (GMXe) submodel. AERCHEM allows the representation of non-equilibrium aqueous-phase chemistry of varying complexity in deliquescent fine aerosols. We perform a global simulation for the year 2010 by using the available detailed kinetic model for the chemistry of inorganic and small oxygenated organics. We evaluate AERCHEM’s performance by comparing the simulated concentrations of sulfate, nitrate, ammonium, and chloride to in situ measurements of three monitoring networks. Overall, AERCHEM reproduces observed concentrations reasonably well. We find that especially in the USA, the consideration of non-equilibrium chemistry in deliquescent aerosols reduces the model bias for sulfate, nitrate, and ammonium, when compared to simulated concentrations by ISORROPIA-II. Over most continental regions, fine aerosol acidity simulated by AERCHEM is similar to the predictions by ISORROPIA-II but tends to simulate slightly lower aerosol acidity in most regions. The consideration of non-equilibrium chemistry in deliquescent aerosols leads to a significant higher aerosol acidity in the marine boundary layer, which is in line with observations and recent literature. AERCHEM allows investigating the global-scale impact of aerosol non-equilibrium chemistry on atmospheric composition. This will aid the exploration of key multiphase processes and improve the model predictions for oxidation capacity and aerosols in the troposphere.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on "How non-equilibrium aerosol chemistry impacts particle acidity...."', Anonymous Referee #1, 04 Dec 2023
This manuscript provides a description of a newly developed sub-submodel module, AERCHEM, designed to enable representation of non-equilibrium aqueous-phase chemistry within the Modular Earth Submodel System (MESSy) Global Modal-aerosol eXtension (GMXe). The first half of the manuscript explains the how AERCHEM functions, its components, and its place within the larger submodel, while the second half of the manuscript benchmarks the model output by comparing it to observations and equilibrium-based simulations of aerosol inorganic components and pH. The manuscript is well written and provides a helpful while still brief summary of how AERCHEM works. However, the usefulness of the second half of the manuscript related to model performance is severely limited by the lack of specifics, as described in more detail below.
Most notably, indicators of overall model bias and, more importantly, the change in model bias between ISORROPIA and AERCHEM are entirely qualitative, not quantitative. Aside from what's visible (but still lacks quotable numbers) in Figure 2 and mentions in the manuscript of where the model-measurement deviation exceeds a factor of two, all comparisons between simulated and observed values or values between two different simulations are rendered in broad, general terms like "reproduces observations reasonably well", "reduces the model bias" [without numbers], "similar to the predictions of ISORROPIA" (taken just from lines 10-13). Researchers interested in using this submodel will not be satisfied with such generalities; to make this useful to future users, model biases and uncertainties for each of the analyzed model outputs (or at least the inorganic aerosol composition, since pH observations come with so much uncertainty that their quantification is always a bit of a guess) should be quantified and clearly reported. What, for example, was the normalized mean bias of simulated sulfate relative to global observations with ISORROPIA, and how much did that change with AERCHEM?
Furthermore, most model users will want to focus on specific regions and times for such purposes as comparing simulated loadings of aerosol species to measurements. To that end, It would be particularly helpful to quantify the model bias for each species by region, rather than the general discussion presently in the manuscript. For nitrate in particular, strongly temperature-dependent partitioning means that biases could be very different season to season, and it would be highly useful to see this broken down more; however, such model outputs may not exist from the single runs performed here, and it may not be worth running a whole additional simulation just to get this new seasonal breakdown.
The most useful, but perhaps most difficult, addition of quantitative information to the manuscript would be to include concrete numbers of how much specific processes contribute to the changes between ISORROPIA and AERCHEM outcomes in the model. For example, how much of the higher acidity of sea-salt particles in AERCHEM is contributed by chloride + OH oxidation, how much from methanesulfonic acid, and how much from other pathways? This may also not be possible to calculate from existing model output and considered beyond the scope of the current manuscript, but I would encourage the authors to revisit the statements made throughout the manuscript on attribution of changes and provide as much quantitative detail as possible.
Further more minor comments are accompanied by line numbers referring to their position in the manuscript.
L 14 - "significant" should be "significantly"
L 67-69 - this isn't a sentence; "distributions. Three" should either be turned into one sentence by replacing the period with a colon, or an independent clause should be added to the fragment.
L 261 - higher than a factor of two relative to what.
L 263-266 - despite the issues with coarse model resolution, can the model comparison with the Jungfraujoch station tell us anything about free tropospheric aerosol composition and how well it'l simulated?
L 273-274 - This sentence isn't clear. Are you saying that the HONO and ClNO2 production are included in AERCHEM but aren't producing as big a model reduction in nitrate as you'd expect? Or that future updates including these reactions would reduce model overpredictions even more?
L 275-276 - Similar question for this sentence -- are these particular organic nitrate hydrolysis reactions included in AERCHEM or not?
L 281 - no comma needed after "even though"
L 281 and 286 - "capable to" should be "capable of"
Section 4.2.3 - To what extent are the differences between ISORROPIA and AERCHEM ammonia just a response to changes in sulfate and nitrate, versus specific facets of new ammonium chemistry?
L 293-294 - Why is this more important? (same issue on L 458).
L 295 - Does "Island" refer to Iceland?
L 296 - "costal" should be "coastal"
L 298-301 - it is surprising that despite these important differences in chlorine chemistry between the two models, the two aerosol modules give similar results for aerosol chloride content (although you haven't quantitatively told us how similar). Does this mean that the hydroxyl radical initiated oxidation of chlorine to insoluble species is unimportant, or is it offset by additional sources?
L 315 - to what extent could this assumption of a unity activity coefficient be biasing results? While it's understandable (as you write in Section 5.1) that the difficulty of estimating activity coefficients means you don't bother to implement them here, it is worth at least some discussion of what effect that might have on results.
L 332 - why does the coarse mode contribute at all to fine mode acidity? Aren't the coarse and fine mode two separate bins? Overall, the discussion of what drives differences in aerosol acidity is confusing, complicated in part by fact that different terms related to acidity (pH, acidity, and alkalinity) are all being intercompared and seemingly used interchangeably.
L 334 - "governed" doesn't seem to fit here. Is this sentence just meant to say that fine particles over the ocean are the category for which simulated pH is most different between the two models?
L 463 - "enhances" should be "enhance"
Figure 2 caption: "Boxes indicate station for which no difference" --> "station" should be "stations", and within what margin is "no difference" calculated? [as an aside, this figure is very pretty!]
SI section 1, first paragraph: "xylens" should be "xylene", or maybe "xylenes" if you're referring to multiple isomers
Citation: https://doi.org/10.5194/egusphere-2023-2587-RC1 -
AC1: 'Reply on RC1', Simon Rosanka, 08 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2587/egusphere-2023-2587-AC1-supplement.pdf
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AC1: 'Reply on RC1', Simon Rosanka, 08 Feb 2024
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RC2: 'Comment on egusphere-2023-2587', Anonymous Referee #2, 11 Dec 2023
The manuscript presents the new development of AERCHEM for the representation of non-equilibrium aqueous phase chemical reactions, as an addition to the thermodynamic equilibrium model ISORROPIA-II, in Earth System modelling. The manuscript is well organized by first presenting the different submodels for treatment of atmospheric aerosols, and after that presenting an application of AERCHEM in global simulations of the inorganic aerosol composition including a detailed evaluation against measurements from three monitoring networks. Further, the acidity of aerosols is compared in terms of pH to limited observations of a global dataset. The manuscript is interesting both from a practical viewpoint of mechanism development and from a scientific viewpoint given the relevance of aerosol-cloud interactions for climate. My main concern is the incomplete description of the connection between AERCHEM and the thermodynamic equilibrium computation. The abstract and text describes AERCHEM as an add-on to ISORROPIA-II, meaning that AERCHEM calculations are done in series with the thermodynamic equilibrium calculations. It remains unclear which variables are transferred from ISORROPIA to AERCHEM and what exactly constitutes the difference in the simulations. The manuscript should be revised according to the specific comments and technical remarks below.
Specific Comments:
1.) Please add a section with the description of the coupling AERCHEM – ISORROPIA in GMXe. For example, it seems like aerosol water content is first calculated in ISORROPIA, and that the aerosol water after adding the water uptake of organic constituents is then used as reaction volume for the non-equilibrium reactions in AERCHEM. Further, it is mentioned on page 9 that GMXe first calculates the amount of each gas phase species that is kinetically able to condense onto the aerosols using the aerosol model M7. Then the equilibrium partitioning of gases to the liquid phase happens in ISORROPIA. How is it avoided that this affects the uptake of gases afterwards in AERCHEM? There is already some explanation on page 9, which should be further extended to get the complete picture of the coupling (see point 5 below). Suggest to create an additional schematic illustration of the program flow that illustrates the transfer of variables between the two submodels.
2.) The effect of crustal elements (like potassium) is considered in ISORROPIA, but not in AERCHEM. Does this mean that the difference between AERCHEM and ISORROPIA simulations is (a) the non-equilibrium aqueous phase reactions and (b) the omission of crustal elements associated with dust emissions and biomass burning? The crustal elements do not only increase aerosol pH but also increase nitrate formation, for example, dust aerosols that contain calcium may react with nitric acid to form calcium nitrate, which significantly contributes to nitrate concentrations when dust emission and industrial emissions coincide in a grid cell. In this regard, it would be illuminating to perform one simulation with EMAC excluding the crustal elements considered in ISORROPIA.
3.) I strongly recommended to include a comprehensive graphic panel for the presentation of the comparison of model simulations to observations of the inorganic aerosol composition, showing box-and-whisker plots (min, max, median, 25th percentile, 75th percentile) of observations, AERCHEM, and ISORROPIA. One plot per sulfate, nitrate, ammonium, chloride where each plot includes all observation stations of one monitoring network. This totals to 4 x 3 plots, fitting on two pages.
4.) Section 4.2.1 (Sulfate): Please add information on how much sulfate is produced in clouds compared to the sulfate produced by gas-to-particle conversion and aerosol aqueous phase production.
5.) The paragraph on page 9, starting with “Some of the differences .:.” could be used in the explanation of the connection between ISORROPIA and AERCHEM.
6.) Section 4.2.2 (Nitrate): In several places of this section, EMAC simulations are referred without mentioning whether this was EMAC using either AERCHEM or ISORROPIA or rather EMAC using ISORROPIA. Maybe first state in which world regions only marginal differences were found between the two submodels and then state where the use of AERCHEM results in differences.
7.) P10, Line 279-280: Is the overestimation of ammonium concentrations in the Midwest US connected to the overestimation of low nitrate concentrations?
8.) P12, Line 333-335: It is a bit difficult to understand why fine particle over major deserts simulated with AERCHEM are slightly more alkaline, given that the crustal elements are not incorporated in the aqueous phase chemistry mechanisms of AERCHEM (P13, Line 388-389) but only in the thermodynamic calculations.
Technical Corrections:
P4, L 101: what is “cloud species”?
P4, L 101: should “GMEx” be replaced by “GMXe”?
P4, Line 101-103: “After GMEx and MECCA have calculated all aerosol processes and gas-phase chemistry …”; does this refer to the non-activated particles? Are the aerosol operators and chemistry operators running during the cloud periods?
P12, Line 340-341: “oxidation of chloride by hydroxyl radical”; please provide a global map of the hydroxyl radical concentration in coarse mode.
P12, Line 345 and Line 355: Rather not refer to “prediction skill” when the comparison to observed aerosol acidity should be qualitative, “better agreement with observations” is more adequate here. Please give the mean pH for observations and models for coastal and marine environments.
P14, Line 394-396: “For dust emissions, the assignment of the anions associated with crustal elements is critical for the impact on acidity as the associated cations are only very weak Lewis acids.” Do you mean “associated anions are only very weak Lewis acids”, such as carbonates and silicates? Please rephrase sentence, avoid using “associated” twice in the sentence.
P15, Line 431-433: It should be noted that the presence of titanium in iron-containing mineral dust might enhance iron dissolution from mineral dust. Further, nitric and sulphuric acids will interact with other metal cations in the mineral dust and have a synergistic effect on overall iron mobilization (Hettiarachchi et al., 2018, https://doi.org/10.1021/acs.jpca.7b11320). Ilmenite could be a good proxy for the complexity of iron-containing mineral dust.
Figure 1: ISORROPIA is not depicted in the slices for GMXe.
Citation: https://doi.org/10.5194/egusphere-2023-2587-RC2 -
AC2: 'Reply on RC2', Simon Rosanka, 08 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2587/egusphere-2023-2587-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Simon Rosanka, 08 Feb 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on "How non-equilibrium aerosol chemistry impacts particle acidity...."', Anonymous Referee #1, 04 Dec 2023
This manuscript provides a description of a newly developed sub-submodel module, AERCHEM, designed to enable representation of non-equilibrium aqueous-phase chemistry within the Modular Earth Submodel System (MESSy) Global Modal-aerosol eXtension (GMXe). The first half of the manuscript explains the how AERCHEM functions, its components, and its place within the larger submodel, while the second half of the manuscript benchmarks the model output by comparing it to observations and equilibrium-based simulations of aerosol inorganic components and pH. The manuscript is well written and provides a helpful while still brief summary of how AERCHEM works. However, the usefulness of the second half of the manuscript related to model performance is severely limited by the lack of specifics, as described in more detail below.
Most notably, indicators of overall model bias and, more importantly, the change in model bias between ISORROPIA and AERCHEM are entirely qualitative, not quantitative. Aside from what's visible (but still lacks quotable numbers) in Figure 2 and mentions in the manuscript of where the model-measurement deviation exceeds a factor of two, all comparisons between simulated and observed values or values between two different simulations are rendered in broad, general terms like "reproduces observations reasonably well", "reduces the model bias" [without numbers], "similar to the predictions of ISORROPIA" (taken just from lines 10-13). Researchers interested in using this submodel will not be satisfied with such generalities; to make this useful to future users, model biases and uncertainties for each of the analyzed model outputs (or at least the inorganic aerosol composition, since pH observations come with so much uncertainty that their quantification is always a bit of a guess) should be quantified and clearly reported. What, for example, was the normalized mean bias of simulated sulfate relative to global observations with ISORROPIA, and how much did that change with AERCHEM?
Furthermore, most model users will want to focus on specific regions and times for such purposes as comparing simulated loadings of aerosol species to measurements. To that end, It would be particularly helpful to quantify the model bias for each species by region, rather than the general discussion presently in the manuscript. For nitrate in particular, strongly temperature-dependent partitioning means that biases could be very different season to season, and it would be highly useful to see this broken down more; however, such model outputs may not exist from the single runs performed here, and it may not be worth running a whole additional simulation just to get this new seasonal breakdown.
The most useful, but perhaps most difficult, addition of quantitative information to the manuscript would be to include concrete numbers of how much specific processes contribute to the changes between ISORROPIA and AERCHEM outcomes in the model. For example, how much of the higher acidity of sea-salt particles in AERCHEM is contributed by chloride + OH oxidation, how much from methanesulfonic acid, and how much from other pathways? This may also not be possible to calculate from existing model output and considered beyond the scope of the current manuscript, but I would encourage the authors to revisit the statements made throughout the manuscript on attribution of changes and provide as much quantitative detail as possible.
Further more minor comments are accompanied by line numbers referring to their position in the manuscript.
L 14 - "significant" should be "significantly"
L 67-69 - this isn't a sentence; "distributions. Three" should either be turned into one sentence by replacing the period with a colon, or an independent clause should be added to the fragment.
L 261 - higher than a factor of two relative to what.
L 263-266 - despite the issues with coarse model resolution, can the model comparison with the Jungfraujoch station tell us anything about free tropospheric aerosol composition and how well it'l simulated?
L 273-274 - This sentence isn't clear. Are you saying that the HONO and ClNO2 production are included in AERCHEM but aren't producing as big a model reduction in nitrate as you'd expect? Or that future updates including these reactions would reduce model overpredictions even more?
L 275-276 - Similar question for this sentence -- are these particular organic nitrate hydrolysis reactions included in AERCHEM or not?
L 281 - no comma needed after "even though"
L 281 and 286 - "capable to" should be "capable of"
Section 4.2.3 - To what extent are the differences between ISORROPIA and AERCHEM ammonia just a response to changes in sulfate and nitrate, versus specific facets of new ammonium chemistry?
L 293-294 - Why is this more important? (same issue on L 458).
L 295 - Does "Island" refer to Iceland?
L 296 - "costal" should be "coastal"
L 298-301 - it is surprising that despite these important differences in chlorine chemistry between the two models, the two aerosol modules give similar results for aerosol chloride content (although you haven't quantitatively told us how similar). Does this mean that the hydroxyl radical initiated oxidation of chlorine to insoluble species is unimportant, or is it offset by additional sources?
L 315 - to what extent could this assumption of a unity activity coefficient be biasing results? While it's understandable (as you write in Section 5.1) that the difficulty of estimating activity coefficients means you don't bother to implement them here, it is worth at least some discussion of what effect that might have on results.
L 332 - why does the coarse mode contribute at all to fine mode acidity? Aren't the coarse and fine mode two separate bins? Overall, the discussion of what drives differences in aerosol acidity is confusing, complicated in part by fact that different terms related to acidity (pH, acidity, and alkalinity) are all being intercompared and seemingly used interchangeably.
L 334 - "governed" doesn't seem to fit here. Is this sentence just meant to say that fine particles over the ocean are the category for which simulated pH is most different between the two models?
L 463 - "enhances" should be "enhance"
Figure 2 caption: "Boxes indicate station for which no difference" --> "station" should be "stations", and within what margin is "no difference" calculated? [as an aside, this figure is very pretty!]
SI section 1, first paragraph: "xylens" should be "xylene", or maybe "xylenes" if you're referring to multiple isomers
Citation: https://doi.org/10.5194/egusphere-2023-2587-RC1 -
AC1: 'Reply on RC1', Simon Rosanka, 08 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2587/egusphere-2023-2587-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Simon Rosanka, 08 Feb 2024
-
RC2: 'Comment on egusphere-2023-2587', Anonymous Referee #2, 11 Dec 2023
The manuscript presents the new development of AERCHEM for the representation of non-equilibrium aqueous phase chemical reactions, as an addition to the thermodynamic equilibrium model ISORROPIA-II, in Earth System modelling. The manuscript is well organized by first presenting the different submodels for treatment of atmospheric aerosols, and after that presenting an application of AERCHEM in global simulations of the inorganic aerosol composition including a detailed evaluation against measurements from three monitoring networks. Further, the acidity of aerosols is compared in terms of pH to limited observations of a global dataset. The manuscript is interesting both from a practical viewpoint of mechanism development and from a scientific viewpoint given the relevance of aerosol-cloud interactions for climate. My main concern is the incomplete description of the connection between AERCHEM and the thermodynamic equilibrium computation. The abstract and text describes AERCHEM as an add-on to ISORROPIA-II, meaning that AERCHEM calculations are done in series with the thermodynamic equilibrium calculations. It remains unclear which variables are transferred from ISORROPIA to AERCHEM and what exactly constitutes the difference in the simulations. The manuscript should be revised according to the specific comments and technical remarks below.
Specific Comments:
1.) Please add a section with the description of the coupling AERCHEM – ISORROPIA in GMXe. For example, it seems like aerosol water content is first calculated in ISORROPIA, and that the aerosol water after adding the water uptake of organic constituents is then used as reaction volume for the non-equilibrium reactions in AERCHEM. Further, it is mentioned on page 9 that GMXe first calculates the amount of each gas phase species that is kinetically able to condense onto the aerosols using the aerosol model M7. Then the equilibrium partitioning of gases to the liquid phase happens in ISORROPIA. How is it avoided that this affects the uptake of gases afterwards in AERCHEM? There is already some explanation on page 9, which should be further extended to get the complete picture of the coupling (see point 5 below). Suggest to create an additional schematic illustration of the program flow that illustrates the transfer of variables between the two submodels.
2.) The effect of crustal elements (like potassium) is considered in ISORROPIA, but not in AERCHEM. Does this mean that the difference between AERCHEM and ISORROPIA simulations is (a) the non-equilibrium aqueous phase reactions and (b) the omission of crustal elements associated with dust emissions and biomass burning? The crustal elements do not only increase aerosol pH but also increase nitrate formation, for example, dust aerosols that contain calcium may react with nitric acid to form calcium nitrate, which significantly contributes to nitrate concentrations when dust emission and industrial emissions coincide in a grid cell. In this regard, it would be illuminating to perform one simulation with EMAC excluding the crustal elements considered in ISORROPIA.
3.) I strongly recommended to include a comprehensive graphic panel for the presentation of the comparison of model simulations to observations of the inorganic aerosol composition, showing box-and-whisker plots (min, max, median, 25th percentile, 75th percentile) of observations, AERCHEM, and ISORROPIA. One plot per sulfate, nitrate, ammonium, chloride where each plot includes all observation stations of one monitoring network. This totals to 4 x 3 plots, fitting on two pages.
4.) Section 4.2.1 (Sulfate): Please add information on how much sulfate is produced in clouds compared to the sulfate produced by gas-to-particle conversion and aerosol aqueous phase production.
5.) The paragraph on page 9, starting with “Some of the differences .:.” could be used in the explanation of the connection between ISORROPIA and AERCHEM.
6.) Section 4.2.2 (Nitrate): In several places of this section, EMAC simulations are referred without mentioning whether this was EMAC using either AERCHEM or ISORROPIA or rather EMAC using ISORROPIA. Maybe first state in which world regions only marginal differences were found between the two submodels and then state where the use of AERCHEM results in differences.
7.) P10, Line 279-280: Is the overestimation of ammonium concentrations in the Midwest US connected to the overestimation of low nitrate concentrations?
8.) P12, Line 333-335: It is a bit difficult to understand why fine particle over major deserts simulated with AERCHEM are slightly more alkaline, given that the crustal elements are not incorporated in the aqueous phase chemistry mechanisms of AERCHEM (P13, Line 388-389) but only in the thermodynamic calculations.
Technical Corrections:
P4, L 101: what is “cloud species”?
P4, L 101: should “GMEx” be replaced by “GMXe”?
P4, Line 101-103: “After GMEx and MECCA have calculated all aerosol processes and gas-phase chemistry …”; does this refer to the non-activated particles? Are the aerosol operators and chemistry operators running during the cloud periods?
P12, Line 340-341: “oxidation of chloride by hydroxyl radical”; please provide a global map of the hydroxyl radical concentration in coarse mode.
P12, Line 345 and Line 355: Rather not refer to “prediction skill” when the comparison to observed aerosol acidity should be qualitative, “better agreement with observations” is more adequate here. Please give the mean pH for observations and models for coastal and marine environments.
P14, Line 394-396: “For dust emissions, the assignment of the anions associated with crustal elements is critical for the impact on acidity as the associated cations are only very weak Lewis acids.” Do you mean “associated anions are only very weak Lewis acids”, such as carbonates and silicates? Please rephrase sentence, avoid using “associated” twice in the sentence.
P15, Line 431-433: It should be noted that the presence of titanium in iron-containing mineral dust might enhance iron dissolution from mineral dust. Further, nitric and sulphuric acids will interact with other metal cations in the mineral dust and have a synergistic effect on overall iron mobilization (Hettiarachchi et al., 2018, https://doi.org/10.1021/acs.jpca.7b11320). Ilmenite could be a good proxy for the complexity of iron-containing mineral dust.
Figure 1: ISORROPIA is not depicted in the slices for GMXe.
Citation: https://doi.org/10.5194/egusphere-2023-2587-RC2 -
AC2: 'Reply on RC2', Simon Rosanka, 08 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2587/egusphere-2023-2587-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Simon Rosanka, 08 Feb 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Model simulation data used in "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy" Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli https://doi.org/10.5281/zenodo.10059700
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Holger Tost
Rolf Sander
Patrick Jöckel
Astrid Kerkweg
Domenico Taraborrelli
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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