Uncertainties in fertilizer-induced emissions of soil nitrogen oxide and the associated impacts on ground-level ozone and methane

Gong, Cheng; Wang, Yan; Tian, Hanqin; Kou-Giesbrecht, Sian; Vuichard, Nicolas; Zaehle, Sönke

doi:https://doi.org/10.5194/egusphere-2025-1416

Preprints

https://doi.org/10.5194/egusphere-2025-1416

Preprints

12 May 2025

| 12 May 2025

Uncertainties in fertilizer-induced emissions of soil nitrogen oxide and the associated impacts on ground-level ozone and methane

Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle

Abstract. Natural and agricultural soils are important sources of nitrogen oxides (NO_x), accounting for about 10 %–20 % of the global NO_x emissions. The increased application of nitrogen (N) fertilizer in agriculture has strongly enhanced the N availability of soils in the last several decades, leading to higher soil NO_x emissions. However, the magnitude of the N fertilizer-induced soil NO_x emissions remains poorly constrained due to limited field observations, resulting in divergent estimates. Here we integrate the results from meta-analyses of field manipulation experiments, emission inventories, atmospheric chemistry modelling and terrestrial biosphere modelling to investigate these uncertainties and the associated impacts on ground-level ozone and methane. The estimated present-day global soil NO_x emissions induced by N fertilizer application varies substantially (0.84–2.2 Tg N yr^-1) among different approaches with different spatial patterns. Simulations with the 3-D global chemical transport model GEOS-Chem demonstrate that N fertilization enhances global surface ozone concentrations during summertime in agricultural hotspots, such as North America, western Europe and eastern and southern Asia by 0.3 to 3.3 ppbv. Our results show that such spreads in soil NO_x emissions also affect atmospheric methane concentrations, reducing the global mean by 7.1 ppbv to 16.6 ppbv as indirect consequence of enhanced N fertilizer application. These results highlight the urgent need to improve the predictive understanding of soil NO_x emission responses to fertilizer N inputs and its representation in atmospheric chemistry modelling.

Received: 25 Mar 2025 – Discussion started: 12 May 2025

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Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-1416', Anonymous Referee #1, 18 Jun 2025
Summary: This paper assesses the emissions of soil NOx from several different emissions parameterizations, and the global impact of these emissions on atmospheric chemistry. Overall, the large range of differences in the emissions (both in terms of magnitude and spatial pattern) impacts surface ozone and methane concentrations. I agree with the author’s conclusions that this is an important issue to consider and they raise some interesting points. However, this study felt very cursory - results were presented and discussed in very short text. Adding some depth and further discussion to the paper would strengthen the manuscript.
Major points
Overall, more detail and depth to the paper is needed to make this a valuable contribution to the literature. For example:
Section 2.4: The text mentions that the TBM simulations are only the model mean – why wouldn’t you be interested in including the different members? Isn’t that variability part of examining the physical processes that drive the NOx emissions? At a minimum, the NMIP model spread be shown in Figure 2 instead of just the model mean.

Section 4.2: The impacts on ozone are focused on the summer, yet soil NOx often peaks in the spring in the northern hemisphere. Have the authors evaluated other seasonal aspects of impacts on ozone? More detail about seasonal changes in the emissions and the subsequent ozone changes would enhance the paper.

Section 4.3 – there are some interesting conclusions about the impacts of soil NOx on methane, yet very little in depth discussion on Figure 4 and the implications. How much OH is changing due to these changes and can it be tied to some of the hotspots in Figure 2? Right now, the discussion on this result (which is one of the larger implications of the paper) is less than 10 lines of text! Can the authors provide any insight into the model behavior and spatio-temporal implications of these results?

The global resolution of the chemistry model is 2x2.5 degrees which is relatively coarse. Could you discuss the implications of this resolution for the modeled NOx emissions?

Minor points
Line 79: The authors characterize the BDSNP model that fixes the fertilizer emissions to 1998, and does not represent interannual variability in fertilizer application. My understanding is that this is not what most interactive models use – they do vary the fertilizer application, and the authors may want to include other applications of this model that do include the interannual variability.

Additional detail for the different emissions methods used would be helpful. Specifically:
Line 107: Please clarify the 1.1% - this is the emissions of nitrogen are 1.1% of the mass of fertilizer applied? This could be explained more clearly.

Line 113: For the non-linear approach, could you briefly explain what Equation 1 is based on?

The HaNi dataset could be explained more – for example, there are specifics on the different types of nitrogen loadings, yet it is unclear how these are going to directly impact the resulting emissions.

The title for Section 2.2 could be more clear – perhaps name the emissions inventory you are using?

Line 136, similar to the comment above – the EF value is 0.7% of what?

Line 176- probably more important to list the name of the gas-phase chemical mechanism used, as well as how aerosols are treated in the model than to refer to the KPP.

Figure 1 – it is very difficult to read the yellow line and text, please update. Also, if the NMIP2 run is an ensemble, then could the spread be shown instead of just the model mean.

Figure 2 – If I am understanding this correctly, Figure 2a is only used to calculate emissions for Figure 2d. Figures 2b, 2c, 2e and 2f each use their own version of fertilizer. The caption states that 2a is from the 2019 HaNi dataset, but are the emissions shown only for that representative year (or all years averaged together from Figure 1?)

Lines 230-232: This explanation is rather confusing – more expansion on these conclusions would be helpful as it seems to be an important part of the conclusions.

Line 247 – more discussion on the seasonal dynamics would be helpful here. The text states that soil NOx peaks in the summer, which it might if only temperature were a factor. But other studies that link to fertilizer application are more closely tied to spring soil NOx peaks. Could the authors expand this discussion about the seasonal dynamics?

Line 283: what meta-analyses are being referred to here? Also, can you be more specific about what emission inventory you are using?

Lines 303-304: I think the authors have missed some of the work that have modified the BDSNP for different environmental drivers (e.g., Wang et al. 2021 for temperature, and Huber et al. 2023 for soil moisture). This should be included as well.

Line 328: uncertainties in….? (I think you want to say emission inventories but it would be helpful to be clear)
Citation: https://doi.org/10.5194/egusphere-2025-1416-RC1
- AC1: 'Reply on RC1', Cheng Gong, 20 Aug 2025
  
  Please see our reply in the attached file.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1416-AC1
RC2:
'Comment on egusphere-2025-1416', Anonymous Referee #3, 08 Jul 2025
Comments:
The manuscript titled “Uncertainties in fertilizer-induced emissions of soil nitrogen oxide and the associated impacts on ground-level ozone and methane,” written by Gong et al., quantifies the uncertainties in soil NOx emissions induced by N fertilizer application (SNOx-Fer) using different estimation approaches and investigates the associated impacts on the simulation of global O₃ and CH₄ concentrations. Overall, this manuscript is well-structured, and the conclusion is important. However, I would like to raise two major concerns and several minor suggestions for improvement.
Major concerns:
I can tell by Figures 2, 3, 4, and Section 4.2 that, in general, regions with higher SNOx-Fer have higher O₃ enhancement. Is this an approximately linear relationship? Does this relationship vary across different sensitivity experiments and different regions? Providing a more detailed analysis of the response of the O₃ simulation to NOx estimations would further highlight the importance of this work. The same concern also applies to the OH/CH₄ simulation.

In Lines 125–129, the growing season is defined using monthly-mean 2-meter temperature and leaf area index instead of using some crop calendar datasets. While this approach is straightforward and climate-driven, it may oversimplify the actual crop phenology in diverse agricultural systems. Given that the rates of N inputs are set to zero during the non-growing season, this definition directly controls the temporal pattern of fertilizer application and thus significantly affects the estimates of fertilizer-induced NOx emissions. If crop-specific growing seasons are not distinguishable in this study, the authors should at least discuss the potential implications of this assumption in the discussion section.

Minor points:
Are there any top-down methods for estimating NOx emissions? If so, it would be beneficial for the authors also to describe it in the introduction, allowing for a more comprehensive review of the estimation approaches.

Section 2.2: Consider also adding one or two sentences to describe why this specific inventory is chosen.

Figure 1: Consider merging (a) and (b) into a single figure using a secondary Y-axis for fertilizer input, which would help the reader better interpret the relationship between nitrogen inputs and SNOx-Fer across approaches.

Section 4.2 and 4.3: When reporting changes in O₃ and CH₄ concentrations, consider also providing percentage changes rather than only providing the ppbv changes.

The HaNi dataset provided N inputs for cropland, pasture, and rangeland. Consider also providing the NOx emissions from cropland, pasture, and rangeland in the Supplementary Information.

Given the large differences in simulated surface O₃ concentrations across the different SNOx-Fer estimation methods (e.g., Fig. 3 and 4), it would be valuable to include a brief comparison with surface O₃ observations. While a full validation is beyond the scope of this study, even a qualitative comparison could help indicate which NOx emission estimation method may better reproduce observed O₃ levels in key agricultural regions.
Citation: https://doi.org/10.5194/egusphere-2025-1416-RC2
- AC2: 'Reply on RC2', Cheng Gong, 20 Aug 2025
  
  Please see our reply in the attached file.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1416-AC2

Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle

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https://doi.org/10.5194/egusphere-2025-1416-supplement

Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle

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Short summary

Our results showed substantially varied fertilizer-induced soil NO_x emissions in 2019 from 0.84 to 2.2 Tg N yr^-1 globally. Such variations further lead to 0.3 to 3.3 ppbv summertime ozone enhancement in agricultural hotspot regions and 7.1 ppbv to 16.6 ppbv reductions in global methane concentrations


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