the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Mar 2025
Numerical simulation of nitrous oxide over Asia using regional climate-chemistry-ecology coupling model RegCM-Chem-YIBs
Abstract. Nitrous oxide (N2O) is a significant greenhouse gas that not only contributes to global warming but also depletes the ozone layer. In our study, we enhanced a regional climate-chemistry-ecology model to better understand how N2O is emitted, transported, and dispersed in the atmosphere. We focused on East Asia, South Asia, and Southeast Asia, using two different datasets to analyze the patterns of N2O in 2020. Our model showed good agreement with real-world observations, revealing that N2O levels vary seasonally and spatially. For example, the lowest concentrations were found in June, while the highest were in December. Certain areas, like the North China Plain and the Ganges River Basin, had higher N2O levels. We also found that N2O concentrations decrease with altitude. By validating our model, we gained insights into the complex interactions between N2O emissions and atmospheric processes. This research helps policymakers develop strategies to reduce N2O emissions. In the future, we aim to refine our model further to improve predictions of N2O emissions and distribution, which will support efforts to combat climate change and protect the ozone layer.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-608', Anonymous Referee #1, 10 May 2025
The authors try to integrate N2O emission, consumption and transport processes into a regional climate-chemistry-ecology model and evaluate it using observed N2O column concentrations at six sites in Asia. Though I appreciate their efforts in developing the model and collecting various forcing data, the manuscript, regrettably, does not adhere to the publication standard of the journal. The major concerns are listed as the following:
- Descriptions on how the climate and land models are lacking, particularly N2O processes in the ecosystem model. My understanding is the integrated model has no nitrogen processes, as N2O fluxes from CAMS and EDGAR are used as inputs, rather from internal simulations.
- The manuscript focuses on the seasonal fluctuations of N2O concentrations and concludes that surface concentration is low when surface N2O emissions are low. This is not supported by their figures. Moreover, the seasonal variations across all sites are not “pronounced” as claimed by the authors. The fluctuations are quite small, within about 1~2 ppb, which is minor relative to the N2O concentration. A deep analysis on seasonal fluctuations of atmospheric N2O concentrations can refer to the paper “The Modeled Seasonal Cycles of Surface N2O Fluxes and Atmospheric N2O”.
- One advantage of regional climate models is that they can provide higher-resolution estimates of target variables. Yet there are no figures showing the model obtains N2O concentrations with spatial details.
- The text is not well written. There are many statements explaining specific definitions or terms (e.g., Lines 109-112), which are not closely related to the topic of this manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-608-RC1 - AC1: 'Reply on RC1', xin zeng, 28 Jul 2025
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RC2: 'Comment on egusphere-2025-608', Anonymous Referee #2, 03 Jul 2025
Zeng et al. simulated the emission, transport, and dispersion of N₂O across Asia in 2020 using a regional climate-chemistry-ecology model, driven by two different N₂O datasets CAMS and EDGAR. They concluded that the model showed good agreement with observations in terms of seasonal and spatial patterns of N₂O concentrations and claimed to gain insights into the complex interactions between N₂O and atmospheric processes. While I acknowledge the authors’ efforts in model development and in compiling a wide range of forcing datasets, the manuscript, in its current form, does not meet the publication standards of GMD. I therefore recommend to reject the manuscript in its present form. The major concerns are listed as below:
General:
- The Introduction is not well developed and lacks a clear connection between the identified research gap and the specific research question addressed in this study.The motivation remains weak and unconvincing, and some background statements appear generic or only loosely related to the core topic of N₂O modeling (see specific comments below).
- The current description of the N₂O-related processes in the model is insufficient and not acceptable in its present form for a model development and evaluation paper submitted to GMD. It lacks the necessary detail regarding the mathematical formulations, parameters, and model structure that have been added or modified.For instance, the statement “In this study, we introduce a new species of N₂O into the coupled model, taking into account the emissions, atmospheric transport, and diffusion processes” is far too vague. What exactly is "new"? How are N₂O emissions calculated? What are the mathematical representations of atmospheric transport and diffusion for N₂O in the model? How are these formulations different from those used for other gases, such as CO2 or CH₄?
- The authors have combined the Results and Discussion into a single section, which makes it difficult to distinguish between the presentation of model outputs and the interpretation or broader implications of other findings (e.g., Lines 224-232). This structure limits the clarity and depth of both components. I recommend splitting the Results and Discussion into separate sections. This would not only improve the organization of the manuscript but also provide space for a more focused and in-depth discussion of the results in the context of existing literature, model limitations, and uncertainties.
- The main discussion section primarily highlights the agreement between the simulation results and previous studies, but it lacks a critical assessment of discrepancies or differences. A balanced discussion should also address where and why the model diverges from other findings, which is essential for understanding model behavior and performancein the future studies. Moreover, there is no discussion of model limitations. For a study published in a model development journal, it is important to transparently acknowledge the assumptions, uncertainties, and potential weaknesses in the model framework or input data.
Specific Comments:
L18-22: Authors claim to gain the complex interactions between N₂O emissions and atmospheric processes, as well as to inform strategies for reducing N₂O emissions. However, these claims are not substantiated by the results presented. There is no discussion on the interaction mechanisms between N₂O dynamics and atmospheric processes, nor is there any explanation or evaluation of a developed strategy for N₂O mitigation.
L34-46: This section largely repeats the content of Lines 23-30 and reads more like a general background summary rather than a focused introduction to N₂O-specific modeling. Much of the text is common knowledge for the intended audience and does not add meaningful context to the study. I recommend the authors revise this part to be more concise and directly aligned with the scientific objectives of the paper. Avoid generic statements and instead focus on the specific research gap, methodological innovation, or model improvement being addressed. Redundant wording should be removed to improve clarity and relevance.
L57: Stange et al. (2000) is not the original publication describing the N₂O development within the DNDC model. It would be great to refer to the original foundational work by Li et al. (1992; JGR), which first introduced the N₂O modeling framework in DNDC.
L56-59: The citation of process-based N₂O modeling studies appears outdated. At a minimum, the authors should acknowledge the development and application of dynamic global vegetation models (DGVMs), such as O-CN, CLM, TRIPLEX, ORCHIDEE, LPJ-GUESS, CABLE, and DLEM, which have been widely used to estimate the global N₂O budget, as discussed in Tian et al. (2020). Furthermore, other key approaches, such as the IPCC-recommended emission factor (EF)-based method and atmospheric inversion modeling, are also essential tools for estimating N₂O emissions at regional and global scales. These approaches should be mentioned to provide a more comprehensive and up-to-date overview of current methodologies in the field.
L78-85: The manuscript repeatedly emphasizes the importance of mitigating N₂O emissions and protecting the ozone layer. However, it is unclear whether this study directly addresses these broader issues in a meaningful or actionable way. If not, I recommend the authors avoid overusing such generic and broad statements. Instead, the focus should be on clearly articulating the specific research question being addressed and ensuring that the motivation aligns with the actual scope and objectives of the study. As it stands, the current presentation of the research motivation is not sufficiently compelling or focused.
L86-95: I believe it is necessary to include a paragraph summarizing the progress made by other regional climate–chemistry–ecosystem models (or similar modeling frameworks) regarding N₂O-related process development. This contextual background is important for readers to understand the advances achieved in previous studies and how the current work fits into or improves upon them. Additionally, is there a specific reason why 2020 was selected as the only study year?
L117-122: The ecological module (e.g., YIBs) should include a clear description of how other nitrogen-related dynamic processes, such as biological nitrogen fixation, wet/dry deposition, plant uptake, and hydrological N loss, are represented, especially in relation to N₂O production and loss. Without this level of process-level documentation, readers and future users cannot assess, reproduce, or compare the model implementation, which is a key requirement of GMD.
L124-134: In the Experimental Design section, it is unclear whether any sensitivity analyses were performed to identify which parameters or input drivers (or climate variables) have the greatest influence on the simulated N₂O concentrations at different pressure levels, particularly in comparison with site-level observations. If such tests were conducted, please specify which sensitivity analysis method was used (e.g., one-at-a-time, Monte Carlo, Sobol, etc.) and summarize the key findings. Additionally, It would be better to clarify how the model was spun up prior to the start of the 2020 experiment or protocol runs. Details on the spin-up duration, initialization datasets, and criteria for equilibrium should be provided.
L203-207: I believe this part belongs to “Methodology” section.
L243-262: The evaluation of temperature, humidity, and wind in the model reads somewhat abrupt, as it appears without prior explanation or context. It is unclear why these three variables are evaluated alongside N₂O concentrations. I guess these are key climatic drivers influencing the simulated N₂O at different pressure levels? this should be clearly justified upfront. Ideally, the authors should first demonstrate the strength of their influence on N₂O through sensitivity analyses (as previously suggested), and then present the evaluation results accordingly.
L270-281: The presentation of the site-level comparison results is overly descriptive and lacks in-depth analysis. I recommend the authors focus more on the discrepancies in seasonality between simulations and observations, which would be more interesting for readers. For instance, why does the model fail to reproduce the N₂O peak in June at sites such as AMY, GSN, and TAP? Additionally, what causes the simulated N₂O concentrations driven by CAMS to be consistently higher than those driven by EDGAR at TAP, UUM, and WLG?
L309-310 & L336-338: This sentence is repetitive and does not add new information, consider removing it.
Citation: https://doi.org/10.5194/egusphere-2025-608-RC2 - AC2: 'Reply on RC2', xin zeng, 28 Jul 2025
- AC3: 'Comment on egusphere-2025-608', xin zeng, 28 Jul 2025
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