the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Dec 2024
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Abstract. Nitrogen (N) transformation processes by soil microbes account for significant nitrous oxide (N2O) emissions from natural ecosystems and cropland. However, understanding and quantifying global soil N2O emissions and their responses to changing environmental conditions remain challenging. We implemented a soil nitrification-denitrification module into the dynamic vegetation model LPJ-GUESS to estimate N2O emissions from global lands. The performance of this new development is examined using observed N2O fluxes from natural soil and cropland field trials, and independent global-scale estimates. LPJ-GUESS broadly reproduces the cumulative N2O emissions under different climate conditions and N fertilizer applications that are observed in the field experiments, with some deviations in emission seasonality. Globally, simulated soil N2O emissions from terrestrial ecosystems increase from 5.6±0.2 Tg N yr-1 in the 1960s to 9.9±0.3 Tg N yr-1 in the 2010s, with croplands contributing about two thirds of the total increase. East Asia and South Asia show the fastest growth rates in N2O emissions over the study period due to the expansion of fertilized croplands. On a global scale, N fertilization (including synthetic fertilizer and manure use), atmospheric N deposition, and climate change contribute 58 %, 46 %, and 24 %, respectively, to the simulated soil N2O emissions in the 2010s. Rising CO2 levels in the atmosphere reduce the simulated emissions by 32 % through increased plant N uptake, whereas land-use changes have varied spatial effects on emissions depending on N management intensity after land-cover conversion. Our estimates only account for the direct soil N2O emissions, excluding those from fertilized pasture. This study highlights the importance of environmental factors in influencing global soil N2O emissions, particularly for assessing greenhouse gas mitigation potential in agricultural ecosystems.
- Preprint
(6220 KB) - Metadata XML
-
Supplement
(1928 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on gmd-2024-223', Anonymous Referee #1, 22 Jan 2025
General:
The paper under my reviewing addresses an important topic and contributes to improving the understanding of soil N2O emissions from global land ecosystems. Overall, the authors have done a great job. They integrated soil nitrification-denitrification processes into the DGVM LPJ-GUESS model, clearly explaining and documenting the added mathematical formulations and parameters, although some complex processes were simplified during the development due to the “large-scale” model structure. Additionally, they examined the model’s performance using a valuable site-level data set and quantified the environmental factors driving changes in global N2O emissions among terrestrial ecosystems through model-based scenario analyses. I think this topic is interesting and of great relevance to GMD. The manuscript is in general well-written and easy to follow. However, a few pieces of information are missing, and some sections require minor adjustments before it can be accepted for publication. Below, I provide specific comments, which I hope will help further improve the manuscript.
comments and suggestions:
The introduction is well stated. The description is sufficient to understand what has been developed in previous DGVM studies in terms of N2O-related processes. However, the organization in some parts can be improved.
LN65-66: 0.2-1.8% of what? Is it the proportion of N fertilization lost as N2O?
LN97-100: What specific cropland management practices are included in your model? How are these practices set up in the global simulations? Since conservation agriculture can significantly influence N2O fluxes in the fields, even though most practices mitigate soil CO2 emissions by enhancing soil C storage. It is better the specify the management setups somewhere in the methodology section.
Fig.1: In nitrification processes, where is the heterotrophic pathway? How do you consider this in the model depending on DON? This is an important process in acidic soils or environments where autotrophic nitrification is less dominant. It should be mentioned in the model description.
LN191-195: Maybe the 38℃ is needed further discussion to represent the entire nitrification process, as NOB bacteria significantly favor higher temperatures compared to AOB and AOA.
LN420-424: It would be valuable to compare crop yields (or N use efficiency). For instance, in the high N fertilization scenario, was the modelled yield lower than the observed yield? If so, this could suggest that less N was taken up, resulting in less N removal from the system and leaving more N to be emitted as N2O.
LN450-453: I would suggest to add the observed WFPS to Fig. S3 to clearly compare the mismatch between the simulation and observation, if the reported data are available.
LN482-484 and Fig.5d: It could be interesting to identify which thin-dashed blue WFPS corresponds to each N treatment. Does the N250 scenario, with the highest N, use the most water in the soil?
LN545-547: I seems to find an explanation for the reduced N2O emissions under elevated CO2 concentrations.
LN651-660: For large-scale applications, fertilizer type-usually simplified as the ratio of NH4 to NO3 in the model-is one of the factors contributing to the simulated uncertainty in N2O emissions. Does LPJ-GUESS actually account for this when performing the S5 runs ("Const_Nfert")?
Citation: https://doi.org/10.5194/gmd-2024-223-RC1 - AC1: 'Reply on RC1', Jianyong Ma, 16 Feb 2025
-
RC2: 'Comment on gmd-2024-223', Anonymous Referee #2, 07 Feb 2025
Ma et al. describe a new parametrization for soil mineral N transformation in the widely used DGVM LPJ-GUESS model. They mainly take NH3 volatilization, nitrification, and denitrification processes into account, with well explaining the mechanistic parametrization of N dynamics. They also provide a global evaluation of the model development in terms of simulated N2O fluxes among land ecosystems. Overall this is a well written paper with a good structure in simulation experiment design and assessment results. Although modelling soil N2O emissions is a challenging work with large uncertainties, authors do a good job by giving a solid discussion on model limitations of N transformation which are well referenced. I think this is an exciting and timely work that greatly advances existing modeling tool and will benefit to biogeochemical research community, it can be accepted by GMD for publication after minor adjustments.
Specifically:
The new model development section is clear and thorough; however, the model simulation protocol needs to be further clarified. For instance, in sections 2.3.1 and 2.3.2, are the forcing data (e.g., climate and N deposition) the same for the site-specific and global runs? Additionally, how does LPJG estimate the dynamics of soil pH during N transformation processes? A brief description of the model's representation of soil physical properties should also be included in section 2.3.
The site-level evaluation is convincing, but the global-scale results require further discussion. Based on Figures 6 and 7, the simulated N2O emissions from pastures are not entirely convincing. It would be expected to see higher N2O fluxes from pastures starting in the 1980s onward, given the increased use of manure and fertilizers during this period. Additionally, how does the model account for manure deposition from livestock waste on grazing pastures? This aspect should be clarified to better understand the model's representation of pasture emissions.
Line78: “... (DGVMs)”, a citation of DGVM would be appreciated.
Line 119 “...of a proportion of the aboveground biomass”, What is the proportion used in the model? It appears to be a dynamic variable that varies depending on the study region, but what is the default value applied in your global simulation? This information is important for understanding the baseline assumptions and how they influence the results.
Line347: Could you provide specific details on how the soil physical characteristics are set up at the study sites? Are these characteristics treated as constant throughout the experimental periods, or are they modeled as time-dynamic variables?
Figure 4c: Why is there such a large mismatch between the simulation and observations in April?
Sect. 3.1.2: What is the soil texture at the study sites? In most Dynamic Global Vegetation Models, the simulated WFPS is highly sensitive to soil texture, with very fine soils typically exhibiting higher WFPS. An accurate representation of hydrology in the model would significantly improve the simulation of all N2O-related processes that depend on WFPS (see your Figure 1). Since different soil texture classes undoubtedly affect WFPS, a brief explanation of how WFPS is associated with soil texture in your model would be greatly appreciated.
Figure 7: Here again, the low N2O emission on global grazing pasture makes non-sense to me due to the livestock waste inputs and N fertilizer use in the recent decades.
Line745-752: How do N2O emissions in the model respond to different agricultural management practices, such as fertilizer application, tillage, or crop rotation? How closely do the simulated results align with real-world observations? Additionally, are these management practices explicitly included in your global simulations? If not, I think your discussion is not strongly supported.
Citation: https://doi.org/10.5194/gmd-2024-223-RC2 - AC2: 'Reply on RC2', Jianyong Ma, 16 Feb 2025
Data sets
CRUJRA v2.4: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data Ian Harris and Shinya Kobayashi https://data.ceda.ac.uk/badc/cru/data/cru_jra/cru_jra_2.4
HILDA+ Global Land Use Change between 1960 and 2019 Karina Winkler et al. https://doi.pangaea.de/10.1594/PANGAEA.921846
MIRCA2000 Felix T. Portmann, Stefan Siebert, and Petra Döll https://zenodo.org/records/7422506
Soil N2O emissions from global land ecosystems simulated by the LPJ-GUESS model Jianyong Ma and Stefan Olin https://zenodo.org/records/14169306
Model code and software
LPJ-GUESS model used for soil N2O simulation (v4.1) Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, and Stefan Olin https://zenodo.org/records/14258279
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 254 | 67 | 10 | 331 | 30 | 10 | 10 |
- HTML: 254
- PDF: 67
- XML: 10
- Total: 331
- Supplement: 30
- BibTeX: 10
- EndNote: 10
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1