Reviews and syntheses: Process-based modeling of the CO<sub>2</sub>:CH<sub>4</sub> production ratio is important for predicting future Arctic methane emissions

Moser, Marius; Kaiser, Lara; Brovkin, Victor; Beer, Christian

doi:10.5194/egusphere-2025-3159

Preprints

https://doi.org/10.5194/egusphere-2025-3159

Preprints

18 Jul 2025

| 18 Jul 2025

Reviews and syntheses: Process-based modeling of the CO₂:CH₄ production ratio is important for predicting future Arctic methane emissions

Marius Moser, Lara Kaiser, Victor Brovkin, and Christian Beer

Abstract. Thawing permafrost in the Arctic threatens to potentially release large amounts of decomposed organic matter as CO₂ or CH₄ to the atmosphere. Predicting the ratio of emitted CO₂ to CH₄ is imperative for reliable future projections. Here, we review the recent literature concerning methanogenesis, and its current representation in both land surface models (LSMs) and the state-of-the-art process-based methane models. We found that the key processes, required to capture the dynamics of the CO₂:CH₄ production ratio, are: fermentation, hydrogenotrophic methanogenesis, and acetoclastic methanogenesis. Commonly discussed linked processes are Fe(III)-reduction and homoacetogenesis. Environmental factors influencing these processes, as identified in the literature, are: temperature, pH, water table position and alternative electron acceptors. While modern process-based methane models account for most of these factors and processes, the same is not true for the simplified methane formulations in many LSMs, which often opt for pre-set parameters that define a constant share of methane production from anaerobic decomposition. This static approach stands in opposition to the growing amount of lab and in-situ data, which suggest a high degree of spatio-temporal variability concerning this ratio, thus preventing its accurate prediction in a changing future Arctic. The challenge lies in upscaling the data as the environmental factors are barely quantified at the pan-Arctic scale. Additionally, there remains the important challenge of how to model and parameterize the temperature dependence of the individual underlying processes. Going forward, these challenges need to be overcome in order to reliably project the CO₂:CH₄ production ratio and methane emissions on larger scales. This will require a more process-based approach of methanogenesis in LSMs, for which we suggest a baseline concept here.

Received: 02 Jul 2025 – Discussion started: 18 Jul 2025

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Journal article(s) based on this preprint

22 Jan 2026

Reviews and syntheses: The role of process-based modeling of the CO₂ : CH₄ production ratio in predicting future terrestrial Arctic methane emissions

Marius Moser, Lara Kaiser, Victor Brovkin, and Christian Beer

Biogeosciences, 23, 605–621, https://doi.org/10.5194/bg-23-605-2026,https://doi.org/10.5194/bg-23-605-2026, 2026

Short summary

Marius Moser, Lara Kaiser, Victor Brovkin, and Christian Beer

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-3159', Anonymous Referee #1, 15 Aug 2025

The paper by Moser et al. is a review on the representation of methane production in process-based models. I think that in principle this is a decent overview that clearly addresses the overly simplistic way in which some models have traditionally modeled methanogenesis, while also proposing ways forward. Still, I have a number of comments for improvement and some thoughts to reflect upon.
First of all, there are many models discussed in this paper but what I’m missing is an overview table showing all LSMs and process-based methane models, and which processes are included in each. This would be really helpful to show which models are leading or still lacking. Similarly, when discussing equations, it would be good to show them. At the very least the basic Q₁₀ and Arrhenius-type equations used by most models.
Otherwise, given the paper’s title and conclusion, I don’t know why the paper decided to focus only on how the models represent methane production, because this is not the only uncertainty related to modeling Arctic methane emissions. Simply knowing where wetlands are located is perhaps one of the largest uncertainties in calculating Arctic methane emissions, as most recently shown by Ying et al. (2025). Correctly simulating snow cover is also very important to accurately simulate soil temperature and soil water content, which in turn affect soil biogeochemistry (Pongracz et al. 2021). Abrupt thaw processes are only mentioned once, even though they can completely transform landscapes and therefore strongly affect methanogenesis.
I am not suggesting that the authors include a complete overview of the uncertainties for spatial upscaling, but the title claims that the CO₂:CH₄ production ratio is important to predict future Arctic methane emissions. Is this true when compared to accurately simulating the environmental drivers that govern methane production and consumption? I agree that we need to include the right biogeochemical processes, but if the ecological and environmental boundary conditions are misrepresented in the model then the output will be wrong regardless. I hope the authors can reflect on this, because this paper mostly gives qualitative evidence to support the importance of the CO₂:CH₄ ratio. Perhaps add some text that delves deeper into how this relates to other types of uncertainty in modeling Arctic methane emissions.
One more nit-pick about the title is that it should be specified that this paper focuses on terrestrial methane emissions, not other methane sources in the Arctic such as lakes, geological sources, wildfires, and rivers and streams (or the ocean, for that matter). Parmentier et al. (2024) showed that these other sources can contribute over 30% of the Arctic methane budget.
In addition, there are a couple of processes that are not discussed but might be important in the Arctic methane budget. In a region where the cold season lasts for most of the year, winter emissions can become quite significant (Zona et al. 2016; Treat et al. 2018), but this is not discussed in the paper. For example, burst-like emissions upon freeze-in are a physical process that locally can lead to very high emissions (Mastepanov et al. 2008). While not directly related to methanogenesis, this is not implemented in any model and also not discussed in the paper. It shows that transport is still highly uncertain, despite what’s claimed on line 66-67. See also Ito et al. (2023) for a nice overview of how the models currently represent cold season fluxes.
High-affinity methanogens are discussed briefly in the manuscript (line 338-341) but I think that these also warrant more discussion. The authors say that this is yet to be explored in most models but neglect to cite Oh et al. (2020) who simulated these methanotrophs and showed that the high latitude methane sink strongly increased as a result, thus reducing net emissions by ~5.5 Tg CH₄ yr^-1. These numbers are uncertain of course, but they stress the need to also focus on methanotrophy, not just methanogenesis. Btw, Oh et al. used a modified version of the Terrestrial Ecosystem Model (TEM), which I think is not mentioned in this paper despite a long history in modeling northern methane budgets (e.g. Zhuang et al. 2004; 2015), and one of the inspirations for the CLM(4Me) model mentioned in this paper. Might be useful to add.
I know that the authors aim to focus on methanogenesis in particular, and I understand the wish for that focus, but the issues I mention above are also important to predict future Arctic methane emissions. Whether the CO₂:CH₄ production ratio is more important is unclear from this paper. For example, Sulman et al. (2022) did show an effect of Fe(III) reduction on these ratios, but the overall effect on emissions was relatively minor. Either a quantification of process importance or an uncertainty analysis would have been helpful to know whether including the extra complexity in the model, as suggested in Figure 2, really would lead to an important increase in model performance and reliably predict future methane emissions in the Arctic. If the authors are unable to better quantify this importance, then I suggest that the title and conclusions are adjusted accordingly.
Minor comments:
Line 42-44: are these both weight and molar ratios? Since C-CO₂:C-CH₄ and CO₂:CH₄ are both mentioned. I recommend converting these to the same unit for better comparison.
Line 90: please show examples of a Q₁₀ and Arrhenius-type equation in the text. Preferably with an example plot of how they differ.
Line 144: very minor comment: maybe change “current” to “latest” (since it’s been a couple of years)
Line 161: which CMIP models use the scheme from CLM(4Me)?
Line 194-195: can you name these models here, and not just the references? Good to add to an overview table.
Line 229: which process-based model?
Line 292-293: is this 40% of permafrost thaw emissions in the form of CO₂ or CH₄?
Line 320: typo: “Arcitc”

References

Ito, A., Li, T., Qin, Z., Melton, J. R., Tian, H., Kleinen, T., et al. (2023). Cold-Season Methane Fluxes Simulated by GCP-CH4 Models. Geophysical Research Letters, 50(14), e2023GL103037. https://doi.org/10.1029/2023GL103037
Mastepanov, M., Sigsgaard, C., Dlugokencky, E. J., Houweling, S., Ström, L., Tamstorf, M. P., & Christensen, T. R. (2008). Large tundra methane burst during onset of freezing. Nature, 456(7222), 628–630. https://doi.org/10.1038/nature07464
Parmentier, F.-J. W., Thornton, B. F., Silyakova, A., & Christensen, T. R. (2024). Vulnerability of Arctic-Boreal methane emissions to climate change. Frontiers in Environmental Science, 12. https://doi.org/10.3389/fenvs.2024.1460155
Pongracz, A., Wårlind, D., Miller, P. A., & Parmentier, F.-J. W. (2021). Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS. Biogeosciences, 18(20), 5767–5787. https://doi.org/10.5194/bg-18-5767-2021
Treat, C. C., Bloom, A. A., & Marushchak, M. E. (2018). Nongrowing season methane emissions–a significant component of annual emissions across northern ecosystems. Global Change Biology, 44, 163. https://doi.org/10.1111/gcb.14137
Ying, Q., Poulter, B., Watts, J. D., Arndt, K. A., Virkkala, A.-M., Bruhwiler, L., et al. (2025). WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022. Earth System Science Data, 17(6), 2507–2534. https://doi.org/10.5194/essd-17-2507-2025
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E., et al. (2016). Cold season emissions dominate the Arctic tundra methane budget. Proceedings of the National Academy of Sciences, 113(1), 40–45. https://doi.org/10.1073/pnas.1516017113
Zhuang, Q., Melillo, J. M., Kicklighter, D. W., Prinn, R. G., McGuire, A. D., Steudler, P. A., et al. (2004). Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: A retrospective analysis with a process-based biogeochemistry model. Global Biogeochemical Cycles, 18(3), GB3010. https://doi.org/10.1029/2004GB002239
Zhuang, Q., Zhu, X., He, Y., Prigent, C., Melillo, J. M., McGuire, A. D., et al. (2015). Influence of changes in wetland inundation extent on net fluxes of carbon dioxide and methane in northern high latitudes from 1993 to 2004. Environmental Research Letters, 10(9), 095009. https://doi.org/10.1088/1748-9326/10/9/095009

Citation: https://doi.org/10.5194/egusphere-2025-3159-RC1
- AC1: 'Reply on RC1', Marius Moser, 29 Oct 2025
  
  We thank this reviewer for taking the time to carefully read our paper and to write this constructive review. The comments and thoughts provided helped to improve our manuscript. We will reply to your individual comments in detail in the supplemented pdf file, with the author’s response in blue underneath the respective comment.
  
  Citation: https://doi.org/10.5194/egusphere-2025-3159-AC1
RC2:
'Comment on egusphere-2025-3159', Guy Schurgers, 18 Sep 2025

Marius Moser et al., "Process-based modeling of the CO₂:CH₄ production ratio is important for predicting future Arctic methane emissions"
The manuscript by Marius Moser and coauthors provides a review of model representations of methanogenesis (CH₄ production) in site-scale models and land surface schemes. It highlights the pathways of CH₄ production and their difference in production of CO₂ and CH₄, and it highlights the importance of capturing the ratio between these two compounds for accurate assessments of climate impacts, with a focus on the Arctic.
The manuscript provides a good overview of the literature on this subject and is well-written, and the overview provided and comparison of model implementations is of interest to the modelling community. After some modifications, I would like to recommend it for publication in Biogeosciences.
However, this does not necessarily mean that I agree with the proposed strategy of refining models with this information; I see major hurdles in the scaling of information that is primarily derived at laboratory scale to models that work at field scale or even grid cells of tens of kilometers. I would like to suggest the authors to discuss this challenge in greater depth (see my comments on section 5 below). I provide further suggestions and comments, hoping that these can help to strengthen the structuring and the impact of the paper even further.

Major recommendations:
I would recommend to bring the introduction of the different pathways (which now starts at l. 117) forward in the text. You bring up hydrogenotrophic and acetoclastic methanogenesis already in the first paragraph of section 2. Because the pathways are so fundamental for understanding your argumentation, I would suggest to start with an explanation of those in section 2. The pathways could probably be illustrated in a simplified way, e.g. as done in Fig. 1.
In section 4, the distinction between LSMs and process-based models in the paper seems somewhat arbitrary - e.g., I would group some of the models in this section also under LSMs (e.g. ISBA-LSM). Maybe it would be better to distinguish levels of complexity in different models, or application to site/point studies vs. application to regional (gridded) simulations.
In section 5, I think that the authors could do a better effort to bring the full complexity of the system into play. While the CO₂:CH₄ production ratios in methanogenesis are well represented by the two pathways that are discussed in detail in the study, the CO₂:CH₄ ratios measured in the field are a combination of emissions from methanogenesis as well as emissions from other processes (e.g. CO₂ fluxes from heterotrophic respiration under aerobic conditions), some of which may dominate over the methanogenesis fluxes. The cited papers from Galera et al. (2023) and Schuur et al. (2022) highlight this (and Galera et al. (2023) argues in fact that incubations provide limited information on in situ conditions). It would be worthwhile to enhance the discussion on how to obtain a useful parameterization of these processes at large scales, and on the availability of relevant data for parameterizing and evaluating models (or maybe the authors could provide suggestions for relevant measurements to be undertaken to constrain such models). The discussion of CO₂:CH₄ ratios from methanogenesis and of CO₂:CH₄ ratios as measured in the field (and hence originating from multiple sources) should be disentangled more in section 5.

Some additional suggestions:
- l. 59: The text seems to mix two impacts of vegetation on CH₄ fluxes: (1) The presence and abundance of aerenchyma affecting the transport, and (2) the provision of substrate for methanogenesis. I would recommend to disentangle these two processes a bit further in the text, because the latter is not related to transport (which is what the paragraph deals with), but with production (which is discussed in the paragraph above)
- l. 144: Regarding the representation of CH₄ production in ESMs, I think it is important to note that, in contrast to CO₂ (i.e. in the C4MIP simulations), the CH₄ feedback is not part of the CMIP6 simulations. But I fully support the statement that including CH₄ production and its feedback to the climate system in ESMs would be desirable.
- l. 152: The two methods presented here are not mutually exclusive: the TOPMODEL approach provides a representation of horizontal heterogeneity, whereas the layering provides a representation of vertical heterogeneity. It is great to have both introduced here, but I would recommend not to present them as contrasts.
It is nice to have the most commonly used models presented (l. 158 and further). It would be nice if you could refer here explicitly to the two methods introduced in l. 152, to highlight which models adopt which of the two approaches.
- l. 200: The authors focus her very much on permafrost-affected landscapes, and while the different environments will certainly play a role for the parameterization of the processes, I hope that the focus on the underlying processes, which is argued for in this study, allows (in principle) an application across different environments.

Minor remarks:
- l. 40-46: The paragraph lists a number of incubation studies with different CO₂:CH₄ ratios. How comparable are these incubation studies in their setup - would we expect similar ratios from all studies, or are the differences explained by differences in the experimental setup?

- l. 50: You mention aerenchyma here without explanation - but you provide a good explanation later (l. 55). I would recommend to either remove the term here, or bring the explanation from l. 55 forward to the first time it is mentioned.

- l. 82: The reference to Yvon-Durocher et al. (2014) is given twice in the sentence; one of the two could be removed

- l. 179: "Naturally, the model has ..." It is not clear from the text why this is natural - I trust it is related to the study setup?

- l. 190: Unclear what "proper" relates to here - "properly incorporated"?

- l. 229: "compliment" should read "complement"

Citation: https://doi.org/10.5194/egusphere-2025-3159-RC2
- AC2: 'Reply on RC2', Marius Moser, 29 Oct 2025
  
  We thank you for taking the time to carefully read and review our manuscript and for providing comments that will help to improve our paper. We will reply to each respective comment in detail in the supplemented pdf file. The reviewer’s comments are written in black font while the author’s response is in blue.
  
  Citation: https://doi.org/10.5194/egusphere-2025-3159-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-3159', Anonymous Referee #1, 15 Aug 2025

The paper by Moser et al. is a review on the representation of methane production in process-based models. I think that in principle this is a decent overview that clearly addresses the overly simplistic way in which some models have traditionally modeled methanogenesis, while also proposing ways forward. Still, I have a number of comments for improvement and some thoughts to reflect upon.
First of all, there are many models discussed in this paper but what I’m missing is an overview table showing all LSMs and process-based methane models, and which processes are included in each. This would be really helpful to show which models are leading or still lacking. Similarly, when discussing equations, it would be good to show them. At the very least the basic Q₁₀ and Arrhenius-type equations used by most models.
Otherwise, given the paper’s title and conclusion, I don’t know why the paper decided to focus only on how the models represent methane production, because this is not the only uncertainty related to modeling Arctic methane emissions. Simply knowing where wetlands are located is perhaps one of the largest uncertainties in calculating Arctic methane emissions, as most recently shown by Ying et al. (2025). Correctly simulating snow cover is also very important to accurately simulate soil temperature and soil water content, which in turn affect soil biogeochemistry (Pongracz et al. 2021). Abrupt thaw processes are only mentioned once, even though they can completely transform landscapes and therefore strongly affect methanogenesis.
I am not suggesting that the authors include a complete overview of the uncertainties for spatial upscaling, but the title claims that the CO₂:CH₄ production ratio is important to predict future Arctic methane emissions. Is this true when compared to accurately simulating the environmental drivers that govern methane production and consumption? I agree that we need to include the right biogeochemical processes, but if the ecological and environmental boundary conditions are misrepresented in the model then the output will be wrong regardless. I hope the authors can reflect on this, because this paper mostly gives qualitative evidence to support the importance of the CO₂:CH₄ ratio. Perhaps add some text that delves deeper into how this relates to other types of uncertainty in modeling Arctic methane emissions.
One more nit-pick about the title is that it should be specified that this paper focuses on terrestrial methane emissions, not other methane sources in the Arctic such as lakes, geological sources, wildfires, and rivers and streams (or the ocean, for that matter). Parmentier et al. (2024) showed that these other sources can contribute over 30% of the Arctic methane budget.
In addition, there are a couple of processes that are not discussed but might be important in the Arctic methane budget. In a region where the cold season lasts for most of the year, winter emissions can become quite significant (Zona et al. 2016; Treat et al. 2018), but this is not discussed in the paper. For example, burst-like emissions upon freeze-in are a physical process that locally can lead to very high emissions (Mastepanov et al. 2008). While not directly related to methanogenesis, this is not implemented in any model and also not discussed in the paper. It shows that transport is still highly uncertain, despite what’s claimed on line 66-67. See also Ito et al. (2023) for a nice overview of how the models currently represent cold season fluxes.
High-affinity methanogens are discussed briefly in the manuscript (line 338-341) but I think that these also warrant more discussion. The authors say that this is yet to be explored in most models but neglect to cite Oh et al. (2020) who simulated these methanotrophs and showed that the high latitude methane sink strongly increased as a result, thus reducing net emissions by ~5.5 Tg CH₄ yr^-1. These numbers are uncertain of course, but they stress the need to also focus on methanotrophy, not just methanogenesis. Btw, Oh et al. used a modified version of the Terrestrial Ecosystem Model (TEM), which I think is not mentioned in this paper despite a long history in modeling northern methane budgets (e.g. Zhuang et al. 2004; 2015), and one of the inspirations for the CLM(4Me) model mentioned in this paper. Might be useful to add.
I know that the authors aim to focus on methanogenesis in particular, and I understand the wish for that focus, but the issues I mention above are also important to predict future Arctic methane emissions. Whether the CO₂:CH₄ production ratio is more important is unclear from this paper. For example, Sulman et al. (2022) did show an effect of Fe(III) reduction on these ratios, but the overall effect on emissions was relatively minor. Either a quantification of process importance or an uncertainty analysis would have been helpful to know whether including the extra complexity in the model, as suggested in Figure 2, really would lead to an important increase in model performance and reliably predict future methane emissions in the Arctic. If the authors are unable to better quantify this importance, then I suggest that the title and conclusions are adjusted accordingly.
Minor comments:
Line 42-44: are these both weight and molar ratios? Since C-CO₂:C-CH₄ and CO₂:CH₄ are both mentioned. I recommend converting these to the same unit for better comparison.
Line 90: please show examples of a Q₁₀ and Arrhenius-type equation in the text. Preferably with an example plot of how they differ.
Line 144: very minor comment: maybe change “current” to “latest” (since it’s been a couple of years)
Line 161: which CMIP models use the scheme from CLM(4Me)?
Line 194-195: can you name these models here, and not just the references? Good to add to an overview table.
Line 229: which process-based model?
Line 292-293: is this 40% of permafrost thaw emissions in the form of CO₂ or CH₄?
Line 320: typo: “Arcitc”

References

Ito, A., Li, T., Qin, Z., Melton, J. R., Tian, H., Kleinen, T., et al. (2023). Cold-Season Methane Fluxes Simulated by GCP-CH4 Models. Geophysical Research Letters, 50(14), e2023GL103037. https://doi.org/10.1029/2023GL103037
Mastepanov, M., Sigsgaard, C., Dlugokencky, E. J., Houweling, S., Ström, L., Tamstorf, M. P., & Christensen, T. R. (2008). Large tundra methane burst during onset of freezing. Nature, 456(7222), 628–630. https://doi.org/10.1038/nature07464
Parmentier, F.-J. W., Thornton, B. F., Silyakova, A., & Christensen, T. R. (2024). Vulnerability of Arctic-Boreal methane emissions to climate change. Frontiers in Environmental Science, 12. https://doi.org/10.3389/fenvs.2024.1460155
Pongracz, A., Wårlind, D., Miller, P. A., & Parmentier, F.-J. W. (2021). Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS. Biogeosciences, 18(20), 5767–5787. https://doi.org/10.5194/bg-18-5767-2021
Treat, C. C., Bloom, A. A., & Marushchak, M. E. (2018). Nongrowing season methane emissions–a significant component of annual emissions across northern ecosystems. Global Change Biology, 44, 163. https://doi.org/10.1111/gcb.14137
Ying, Q., Poulter, B., Watts, J. D., Arndt, K. A., Virkkala, A.-M., Bruhwiler, L., et al. (2025). WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022. Earth System Science Data, 17(6), 2507–2534. https://doi.org/10.5194/essd-17-2507-2025
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E., et al. (2016). Cold season emissions dominate the Arctic tundra methane budget. Proceedings of the National Academy of Sciences, 113(1), 40–45. https://doi.org/10.1073/pnas.1516017113
Zhuang, Q., Melillo, J. M., Kicklighter, D. W., Prinn, R. G., McGuire, A. D., Steudler, P. A., et al. (2004). Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: A retrospective analysis with a process-based biogeochemistry model. Global Biogeochemical Cycles, 18(3), GB3010. https://doi.org/10.1029/2004GB002239
Zhuang, Q., Zhu, X., He, Y., Prigent, C., Melillo, J. M., McGuire, A. D., et al. (2015). Influence of changes in wetland inundation extent on net fluxes of carbon dioxide and methane in northern high latitudes from 1993 to 2004. Environmental Research Letters, 10(9), 095009. https://doi.org/10.1088/1748-9326/10/9/095009

Citation: https://doi.org/10.5194/egusphere-2025-3159-RC1
- AC1: 'Reply on RC1', Marius Moser, 29 Oct 2025
  
  We thank this reviewer for taking the time to carefully read our paper and to write this constructive review. The comments and thoughts provided helped to improve our manuscript. We will reply to your individual comments in detail in the supplemented pdf file, with the author’s response in blue underneath the respective comment.
  
  Citation: https://doi.org/10.5194/egusphere-2025-3159-AC1
RC2:
'Comment on egusphere-2025-3159', Guy Schurgers, 18 Sep 2025

Marius Moser et al., "Process-based modeling of the CO₂:CH₄ production ratio is important for predicting future Arctic methane emissions"
The manuscript by Marius Moser and coauthors provides a review of model representations of methanogenesis (CH₄ production) in site-scale models and land surface schemes. It highlights the pathways of CH₄ production and their difference in production of CO₂ and CH₄, and it highlights the importance of capturing the ratio between these two compounds for accurate assessments of climate impacts, with a focus on the Arctic.
The manuscript provides a good overview of the literature on this subject and is well-written, and the overview provided and comparison of model implementations is of interest to the modelling community. After some modifications, I would like to recommend it for publication in Biogeosciences.
However, this does not necessarily mean that I agree with the proposed strategy of refining models with this information; I see major hurdles in the scaling of information that is primarily derived at laboratory scale to models that work at field scale or even grid cells of tens of kilometers. I would like to suggest the authors to discuss this challenge in greater depth (see my comments on section 5 below). I provide further suggestions and comments, hoping that these can help to strengthen the structuring and the impact of the paper even further.

Major recommendations:
I would recommend to bring the introduction of the different pathways (which now starts at l. 117) forward in the text. You bring up hydrogenotrophic and acetoclastic methanogenesis already in the first paragraph of section 2. Because the pathways are so fundamental for understanding your argumentation, I would suggest to start with an explanation of those in section 2. The pathways could probably be illustrated in a simplified way, e.g. as done in Fig. 1.
In section 4, the distinction between LSMs and process-based models in the paper seems somewhat arbitrary - e.g., I would group some of the models in this section also under LSMs (e.g. ISBA-LSM). Maybe it would be better to distinguish levels of complexity in different models, or application to site/point studies vs. application to regional (gridded) simulations.
In section 5, I think that the authors could do a better effort to bring the full complexity of the system into play. While the CO₂:CH₄ production ratios in methanogenesis are well represented by the two pathways that are discussed in detail in the study, the CO₂:CH₄ ratios measured in the field are a combination of emissions from methanogenesis as well as emissions from other processes (e.g. CO₂ fluxes from heterotrophic respiration under aerobic conditions), some of which may dominate over the methanogenesis fluxes. The cited papers from Galera et al. (2023) and Schuur et al. (2022) highlight this (and Galera et al. (2023) argues in fact that incubations provide limited information on in situ conditions). It would be worthwhile to enhance the discussion on how to obtain a useful parameterization of these processes at large scales, and on the availability of relevant data for parameterizing and evaluating models (or maybe the authors could provide suggestions for relevant measurements to be undertaken to constrain such models). The discussion of CO₂:CH₄ ratios from methanogenesis and of CO₂:CH₄ ratios as measured in the field (and hence originating from multiple sources) should be disentangled more in section 5.

Some additional suggestions:
- l. 59: The text seems to mix two impacts of vegetation on CH₄ fluxes: (1) The presence and abundance of aerenchyma affecting the transport, and (2) the provision of substrate for methanogenesis. I would recommend to disentangle these two processes a bit further in the text, because the latter is not related to transport (which is what the paragraph deals with), but with production (which is discussed in the paragraph above)
- l. 144: Regarding the representation of CH₄ production in ESMs, I think it is important to note that, in contrast to CO₂ (i.e. in the C4MIP simulations), the CH₄ feedback is not part of the CMIP6 simulations. But I fully support the statement that including CH₄ production and its feedback to the climate system in ESMs would be desirable.
- l. 152: The two methods presented here are not mutually exclusive: the TOPMODEL approach provides a representation of horizontal heterogeneity, whereas the layering provides a representation of vertical heterogeneity. It is great to have both introduced here, but I would recommend not to present them as contrasts.
It is nice to have the most commonly used models presented (l. 158 and further). It would be nice if you could refer here explicitly to the two methods introduced in l. 152, to highlight which models adopt which of the two approaches.
- l. 200: The authors focus her very much on permafrost-affected landscapes, and while the different environments will certainly play a role for the parameterization of the processes, I hope that the focus on the underlying processes, which is argued for in this study, allows (in principle) an application across different environments.

Minor remarks:
- l. 40-46: The paragraph lists a number of incubation studies with different CO₂:CH₄ ratios. How comparable are these incubation studies in their setup - would we expect similar ratios from all studies, or are the differences explained by differences in the experimental setup?

- l. 50: You mention aerenchyma here without explanation - but you provide a good explanation later (l. 55). I would recommend to either remove the term here, or bring the explanation from l. 55 forward to the first time it is mentioned.

- l. 82: The reference to Yvon-Durocher et al. (2014) is given twice in the sentence; one of the two could be removed

- l. 179: "Naturally, the model has ..." It is not clear from the text why this is natural - I trust it is related to the study setup?

- l. 190: Unclear what "proper" relates to here - "properly incorporated"?

- l. 229: "compliment" should read "complement"

Citation: https://doi.org/10.5194/egusphere-2025-3159-RC2
- AC2: 'Reply on RC2', Marius Moser, 29 Oct 2025
  
  We thank you for taking the time to carefully read and review our manuscript and for providing comments that will help to improve our paper. We will reply to each respective comment in detail in the supplemented pdf file. The reviewer’s comments are written in black font while the author’s response is in blue.
  
  Citation: https://doi.org/10.5194/egusphere-2025-3159-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (13 Nov 2025) by Akihiko Ito

AR by Marius Moser on behalf of the Authors (18 Dec 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (06 Jan 2026) by Akihiko Ito

AR by Marius Moser on behalf of the Authors (07 Jan 2026) Manuscript

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22 Jan 2026

Reviews and syntheses: The role of process-based modeling of the CO₂ : CH₄ production ratio in predicting future terrestrial Arctic methane emissions

Marius Moser, Lara Kaiser, Victor Brovkin, and Christian Beer

Biogeosciences, 23, 605–621, https://doi.org/10.5194/bg-23-605-2026,https://doi.org/10.5194/bg-23-605-2026, 2026

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Marius Moser, Lara Kaiser, Victor Brovkin, and Christian Beer

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Arctic warming might lead to increased carbon dioxide and methane emissions. Process-based prediction of their ratio is key to projecting the future carbon cycle. However, land surface models often assume a constant ratio. To overcome this limitation, we identify three core processes for representing methanogenesis accurately in land surface models: fermentation, acetoclastic methanogenesis, and hydrogenotrophic methanogenesis.


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Reviews and syntheses: Process-based modeling of the CO2:CH4 production ratio is important for predicting future Arctic methane emissions

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Reviews and syntheses: Process-based modeling of the CO₂:CH₄ production ratio is important for predicting future Arctic methane emissions