the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM version 2.10
Makcim L. De Sisto
Andrew H. MacDougall
Nadine Mengis
Sophia Antoniello
Abstract. Nitrogen and phosphorus biogeochemical dynamics are crucial for the regulation of the terrestrial carbon cycle. In Earth System Models (ESMs) the implementation of nutrient limitations has been shown to improve the carbon cycle feedback representation and hence, improve the fidelity of the response of land to simulated atmospheric CO2 rise. Here we aimed to implement a terrestrial nitrogen and phosphorus cycle in an Earth system model of intermediate complexity to improve projections of the future CO2 fertilization feedbacks. The nitrogen cycle is an improved version of the Wania et al. (2012) Nitrogen (N) module, with enforcement of N mass conservation and the merger with a deep land-surface and wetland module that allows for the estimation of N2O and NO fluxes. The N cycle module estimates fluxes from three organic (litter, soil organic matter and vegetation) and two inorganic (NH4+ and NO3-) pools, accounts for inputs from biological nitrogen fixation and N deposition. The P cycle module contains the same organic pools with one inorganic P pool, it estimates influx of P from rock weathering and losses from leaching and occlusion. Two historical simulations are carried for the different nutrient limitation setups of the model: carbon and nitrogen (CN) and carbon, nitrogen and phosphorus (CNP), with a baseline carbon only simulation. The improved N cycle module now conserves mass and the added fluxes (NO and N2O), along with the N and P pools are within the range of other studies and literature. The implementation of nutrient limitation resulted in a reduction of GPP from the Carbon-Nitrogen (133 Pg yr-1) and Carbon-Nitrogen-Phosphorus (129 Pg yr-1) simulations by the year 2020, which implies that the model efficiently represents a nutrient limitation over the CO2 fertilization effect. CNP simulation resulted in a reduction of 10 % of the mean GPP and a reduction of 23 % of the vegetation biomass compared to baseline C simulation. These results are in better agreement with observations, particularly in tropical regions where P limitation is known to be important. In summary, the implementation of the nitrogen and phosphorus cycle have successfully enforced a nutrient limitation in the terrestrial system, which now have reduced the primary productivity and the capacity of land to uptake atmospheric carbon better matching observations.
Makcim L. De Sisto et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2022-191', Anonymous Referee #1, 25 Oct 2022
This manuscript describes new nitrogen (N) and phosphorus (P) cycling processes that have been added to a new version of the UVic ESCM intermediate complexity Earth system model. The new processes are well justified, clearly described and well supported by citations to relevant literature, data syntheses, and theory. The new model functions are evaluated using global datasets of carbon, nitrogen, and phosphorus pools and/or fluxes and the level of agreement and areas for improvement in the model are described clearly and fairly. Overall, this is a well-written model description paper.
There are some areas where the clarity of the manuscript could be improved, primarily related to the equations and figures.
While the Methods section does include several relevant equations for N and P cycling, it omits some important processes and overall does not provide a complete picture of N and P cycling in the model. Importantly, equations and explanations are not provided for the variable tissue C:N and C:P ratios that are an important part of the stoichiometric limitation component of the model. I would advise including those equations in the main text since they are a key part of the model functionality and results. I would also suggest adding an appendix with the complete set of equations related to N and P cycling so readers do not need to search through other previous papers to gain a complete picture of how nutrient cycling in the model works.
The figures are generally informative, but there were some parts of the text that described model-data comparisons and other patterns that were not directly shown in the figures. In addition, I think it would improve the readability of the paper if the figures showing N results matched the figures showing P results in their content. Currently, the N figures and P figures show different comparisons in some cases which makes it less straightforward for readers to evaluate those parts of the model. For example, Figure 9 shows maps of modeled total soil P compared with measured total soil P and the difference between them. In contrast, Figure 7 shows maps of modeled N but does not show any direct comparison with measured patterns of N, even though model-data comparisons of global N patterns are discussed in the text.
Finally, the paper describes the P cycle in a way that takes fertilization (which is not included in these simulations) into account, but there is no discussion of the role of anthropogenic N fertilization and whether it could introduce bias in model-data comparisons of the N cycle.
Specific comments:
Line 14: It would be helpful to include the GPP for carbon-only simulations in this comparison as well.
Line 59-61: This list was hard to follow and could use some editing.
Line 69-75: The introduction discusses Earth system models in general, and then the history of UVic ESCM in particular). I think it would be helpful to include a few sentences about intermediate complexity ESMs as a class to provide some more context about the goals of the type of model that UVic ESCM represents and how it compares to other similar models.
Line 105: Variable C:N ratios for leaf and root pools are mentioned but the details (and equations) of what determines the actual C:N ratio are not provided. It would also help to explain here how the variable C:N ratios affect other parts of the model (e.g., photosynthesis or root function dependence on tissue N). It would help to provide some summary of how the relevant processes from the Gerber et al citation are calculated, ideally with equations provided in an appendix.
Equations 1-2: An explanation should be provided here for what “av” means in the mineral nitrogen pools. Later in the paper I found that this means “available” but that should be explained here, along with an explanation of how the available fraction is calculated. Is there an unavailable fraction?
Line 122-123: I found “depth of soil layer” and “root depth” confusing. Is the soil layer referring to each individual model layer, or to the depth of the entire soil? Is the root depth a rooting depth parameter for each PFT, or the depth at which the root fraction is being calculated?
Line 129: Provide an explanation or reference for the statement that “It takes 1 mol of NO3 to mineralize 1 mol of organic C.”
Line 132-133: The temperature and moisture functions are not provided or explained. Is a moisture function necessary when the anaerobic respiration is calculated only for the saturated fraction of the layer?
Table 1: Descriptions should include “pool” or “rate” or similar for each line since the table contains a mix of different types. Also, temperature and moisture functions are functions rather than numbers or outputs and feel out of place in this table.
Table 2: Is DSL the same as D in Equation 3? Make sure the notation is consistent.
Equation 8: Should the Pimm term be negative in this equation? Immobilized P would be subtracted from the inorganic P pool. Also, shouldn’t equation 9 by included as a negative term in Equation 8 since sorption reduces the inorganic P pool? These equations don’t seem to be mass balanced with respect to each other.
Line 186: Is QD here the same as q in equation 6? Both are described as runoff.
Line 189-190: Similar to C:N, the variable C:P ratio of leaf and root tissues is mentioned here but there is no explanation or equation for what controls the value of the ratio.
Line 195: the vegetation P change over time
Line 200-204: Equation 16 needs some conceptual explanation. It’s not directly clear from the equation and description what process this is representing. Are the nitrogen costs related to actual nitrogen availability?
Line 222-224: Equations and a more detailed explanation should be included for stoichiometric limitation, since this is a critical part of how the model works and is key to understanding the results.
Line 232: I did not find an explanation of CPleafmax, CNleafmax, Rleafp, or RleafN in the text or equations showing how the model depends on these parameters. If these parameters are important enough to be the basis for the sensitivity analysis, they should be clearly explained in the text.
Line 259-261: This is not shown in any of the figures. This statement could be supported by showing a map of biomass from the different simulations and the difference from the C-only simulation.
Line 274: Difference in tropical vegetation biomass is also not shown in any figure. This could be shown as a map or an average biomass value by latitude for different simulations.
Line 294: I would reorder the figures so they appear in the order described – 6 is described before 4 and 5.
Line 304-306: This statement should be supported by a figure showing vegetation carbon as a map or latitudinal gradient.
Line 314: Figure 7 does not show the difference in N compared to Wania et al 2012, so this statement cannot be evaluated.
Figure 4: This figure was difficult to interpret because only the differences in PFT fractions were shown, and not the actual fractions. There also is not much explanation of how relative PFT distributions relate to N and P cycling in the model so it’s not clear how relevant this is to the main model developments being described.
Figure 5: There is no explanation of how these correlations are calculated. Is this based on relative amount of all PFTs in each grid cell? This does not seem to be the most useful test of the model since many grid cells are dominated by one or two PFTs. Wouldn’t variation in PFTs across grid cells be a more useful metric to test?
Lines 326-328: There is an order of magnitude range in the different estimates, so they don’t seem like a very strong constraint on the model. Is there any expectation of which set of estimates might be more accurate?
Line 329: Is CN ratio referring to soil, vegetation, or whole ecosystem? Figure 7 is also unclear about this.
Line 336: Equation 16 included some nitrogen cost of phosphatase parameters. Does this not connect the N and P cycles in a way that could allow co-limitation? It’s hard to tell without more explanation of that equation.
Line 341: The model does not include anthropogenic N inputs, so is it reasonable to compare it with estimates that do include anthropogenic inputs? Couldn’t this indicate that the model overestimates natural sources since anthropogenic N inputs in reality are very high?
Line 372-374: Global terrestrial P should be included as a line in Table 6. Table 6 does not indicate estimates from terrestrial P models (or at least does not indicate which estimates are from models versus measurement syntheses). What is the evidence that other models are underestimating P in subsoils and not that this model overestimates P in subsoils?
Figure 10: This figure doesn’t make much sense to me. Why would the model N:P leaf ratio be perfectly linear with respect to latitude? Is the Reich and Oleksyn relationship a simple linear function with respect to latitude? If so, this seems like a very simplistic test of a complex model. Also, it is difficult to interpret this figure because there was no explanation provided for what controls leaf N:P ratio in the model.
Table A1: There is no reason this short table should be in a separate appendix. It’s an important part of the model and should be in the main text.
Citation: https://doi.org/10.5194/gmd-2022-191-RC1 -
RC2: 'Comment on gmd-2022-191', Anonymous Referee #2, 02 Jan 2023
Sisto et al. describe the modifications to the UVic ESCM intermediate complexity Earth system model done to represent new nitrogen (N) and phosphorus (P) cycling processes. The new model is evaluated based on global datasets of C, N, and P pools and fluxes. Incorporating P cycles into ecosystem models is timely and important work. The equations and processes are clearly described. Overall, this manuscript is well-organized and easy to read. However, some concerns need to be addressed or clarified.
The major points are:
Insufficient description of methodology: The model is insufficiently described in 2.4 Nitrogen and phosphorus limitation. The critical aspect of nutrient effects on C cycling is the competition of plants and microbes for limited nutrients. This aspect is not described here. How do you deal with cases in which available soil P and mineralization are insufficient to satisfy the P immobilization demand? In addition, the N, P limitation on the C cycle in vegetation is described too simply; we don’t have enough details. (The Parameters for sensitivity analysis are not described enough in the model structure).
Lack of evaluation: Even though this is an ESM model, this paper focuses more on land surface processes. The evaluation should include different scales, such as site and regional levels. We can only see the global scale values. In addition, do you capture spatial gradients in GPP or seasonal or interannual variation in GPP?
I expect the author to provide an understanding of the model dynamics, such as the C, N, and P processes in vegetation and soil, and how they couple and interact.
Specific points:
Line 68, 71: Some references related to the recent CNP model should include. (Fleischer et al., 2019; Thum et al., 2019; Wang et al., 2020; Yang et al., 2014)
Line 153: It is section 2.4, not 2.5
Line 155: The names of inorganic P pools need to be more consistent. You named the labile P pool, but in figure 1, it is Dissolved Inorganic P. Different models have a different definitions of labile P and dissolved inorganic P, such as (Goll et al., 2017; Thum et al., 2019; Wang et al., 2020; Yang et al., 2014). So, giving clear definitions and keeping the name consistent is better.
Line 170: Which part of the approach is more controllable? Explain it more precisely.
Line 221: In equation 19, if the CN increases, the Vcmax will increase; how is plant productivity reduced? Line 219 describes it in the opposite direction.
Line 224: The NP limitation needs to be clarified. Is it based on Liebig's Law of the Minimum?
Line 248: Phosphorus dataset is Pdataset in the equation (20).
Line 273: Should the baseline have a higher NPP? If I understand it correctly.
Line 277: This part is hard to follow and needs to be articulate.
Line 288: In fig 3, why CN has a larger atmospheric CO2 pool than CNP?
Line 298: Could you describe your mechanisms of dynamic PFT, maybe in the supplement?
Line 329: Tropical areas are supposed to have enough N, so a lower NP ratio.
Line 332: uncertainty (95-730). This is a huge range.
Line 354: I wondered why Fertilization inputs did not use here.
Line 366: “This underestimation is likely the result of a high mineralization rate.” Do you check the soil C pool? And maybe the CP ratio also gives some clues about the underestimation of organic P.
Reference:
Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F., Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M., Haverd, V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn, B. E., Mercado, L. M., Norby, R. J., Pak, B., … Lapola, D. M. (2019). Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nature Geoscience, 12(9), Article 9. https://doi.org/10.1038/s41561-019-0404-9
Goll, D. S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J., Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B., Zaehle, S., Penuelas, J., Janssens, I., & Ciais, P. (2017). A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geoscientific Model Development, 10(10), 3745–3770. https://doi.org/10.5194/gmd-10-3745-2017
Thum, T., Caldararu, S., Engel, J., Kern, M., Pallandt, M., Schnur, R., Yu, L., & Zaehle, S. (2019). A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996). Geoscientific Model Development, 12(11), 4781–4802. https://doi.org/10.5194/gmd-12-4781-2019
Wang, Z., Tian, H., Yang, J., Shi, H., Pan, S., Yao, Y., Banger, K., & Yang, Q. (2020). Coupling of Phosphorus Processes With Carbon and Nitrogen Cycles in the Dynamic Land Ecosystem Model: Model Structure, Parameterization, and Evaluation in Tropical Forests. Journal of Advances in Modeling Earth Systems, 12(10), e2020MS002123. https://doi.org/10.1029/2020MS002123
Yang, X., Thornton, P. E., Ricciuto, D. M., & Post, W. M. (2014). The role of phosphorus dynamics in tropical forests – a modeling study using CLM-CNP. Biogeosciences, 11(6), 1667–1681. https://doi.org/10.5194/bg-11-1667-2014
Citation: https://doi.org/10.5194/gmd-2022-191-RC2 -
RC3: 'Comment on gmd-2022-191', Anonymous Referee #3, 12 Jan 2023
The authors present a model implementation and testing of a terrestrial nitrogen and phosphorus cycle in an Earth system model of intermediate complexity, UVic ESCM version 2.10, to improve projections of the future CO2 fertilization feedbacks. Their research has important reasoning for increasing understanding of global biogeochemical cycles and climate change, the methodology is fine, but the presentation of results is lacking and the model validation is incomplete. I provide more detailed comments below, however I think some revision is needed before a proper evaluation can be completed.
Major comments:
The paper is mostly presenting a new model, which is appropriate for this journal. However, more “science” needs to be presented with the model in order to determine if their new tool is appropriate for further studies at its present state. For example, the authors could provide more model validation and intercomparison with available products and similar models. I appreciate the evaluation of the Keelling curve and FLUXCOM GPP, but this is only a validation of CO2. What about energy? What about water? What about the nutrient cycles?
I suggest adding tool like ILAMB (Collier et al., 2018) for evaluating model performance throughout variables related to the carbon, water and energy cycles, as well as a sensitivity analysis of current with meteorological variables.
Collier, N., Hoffman, F.M., Lawrence, D.M., KeppelâAleks, G., Koven, C.D., Riley, W.J., Mu, M., Randerson, J.T., 2018. The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation. J. Adv. Model. Earth Syst. 10, 2731–2754. https://doi.org/10.1029/2018MS001354
The authors should add comparisons with other similar studies. They do that to an extent (Poulter et al., 2015; He et al., 2021), but there are so many other similar and recent studies that should be added, such as:
Wang, Y., Ciais, P., Goll, D., Huang, Y., Luo, Y., Wang, Y.P., Bloom, A.A., Broquet, G., Hartmann, J., Peng, S., Penuelas, J., Piao, S., Sardans, J., Stocker, B.D., Wang, R., Zaehle, S., Zechmeister-Boltenstern, S., 2018. GOLUM-CNP v1.0: A data-driven modeling of carbon, nitrogen and phosphorus cycles in major terrestrial biomes. Geosci. Model Dev. 11, 3903–3928. https://doi.org/10.5194/gmd-11-3903-2018
Goll, D.S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J., Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B., Zaehle, S., Penuelas, J., Janssens, I., Ciais, P., 2017. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. https://doi.org/10.5194/gmd-10-3745-2017
Braghiere, R.K., Fisher, J.B., Allen, K., Brzostek, E., Shi, M., Yang, X., Ricciuto, D.M., Fisher, R.A., Zhu, Q., Phillips, R.P., 2022. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. e2022MS003204. https://doi.org/10.1029/2022MS003204
The paper needs to discuss ways forward. How can this model be improved? What steps should be taken to this end? Expand the discussion about how this could be linked to other novel methods, such as remote sensing for example? Can you add discussion about parametric vs. structural uncertainty in ESMs? Why does that matter?
Moreover, why is this model needed? Is it just another model on top of the CMIP simulations? A deeper discussion about how this model relates to other models and the future of climate modeling is needed.
Specific comments:
Introduction needs work in properly linking biodiversity with other aspects of the biogeochemical cycles in the Earth system and climate change. I added a few extra references, but a more thorough literature review is required.
Introduction:
I find the introduction a bit shallow and very model centric. I understand this is a modeling journal, but the reader would benefit from more general scientific discussions at the beginning. You may want to cite:
Wieder, W.R., Cleveland, C.C., Smith, W.K., Todd-Brown, K., 2015. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444. https://doi.org/10.1038/ngeo2413
Zaehle, S., Jones, C.D., Houlton, B., Lamarque, J.-F., Robertson, E., 2015. Nitrogen Availability Reduces CMIP5 Projections of Twenty-First-Century Land Carbon Uptake. J. Clim. 28, 2494–2511. https://doi.org/10.1175/JCLI-D-13-00776.1
Line 2: Earth system models (ESMs)
Line 6: Nitrogen (N). It is not the first time the word nitrogen appears. Please define acronyms in first appearance.
Line 29: Earth system models (ESMs)
Line 30: Missing References:
Line 49-52: See also:
Braghiere, R.K., Fisher, J.B., Allen, K., Brzostek, E., Shi, M., Yang, X., Ricciuto, D.M., Fisher, R.A., Zhu, Q., Phillips, R.P., 2022. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. e2022MS003204. https://doi.org/10.1029/2022MS003204
Throughout the text: Either call it N, P, C or write nitrogen, phosphorus, carbon. Be consistent.
Line 63: This isn’t true. Although ESM modeling with phosphorus is indeed limited. See:
Wang, Y., Ciais, P., Goll, D., Huang, Y., Luo, Y., Wang, Y.P., Bloom, A.A., Broquet, G., Hartmann, J., Peng, S., Penuelas, J., Piao, S., Sardans, J., Stocker, B.D., Wang, R., Zaehle, S., Zechmeister-Boltenstern, S., 2018. GOLUM-CNP v1.0: A data-driven modeling of carbon, nitrogen and phosphorus cycles in major terrestrial biomes. Geosci. Model Dev. 11, 3903–3928. https://doi.org/10.5194/gmd-11-3903-2018
Goll, D.S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J., Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B., Zaehle, S., Penuelas, J., Janssens, I., Ciais, P., 2017. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. https://doi.org/10.5194/gmd-10-3745-2017
Braghiere, R.K., Fisher, J.B., Allen, K., Brzostek, E., Shi, M., Yang, X., Ricciuto, D.M., Fisher, R.A., Zhu, Q., Phillips, R.P., 2022. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. e2022MS003204. https://doi.org/10.1029/2022MS003204
Methodology:
Why do you have a schematic of the P cycle but not the N cycle? Be consistent.
Line 105: variable
Line 147: Available N
Line 156: vegetation (not capitalized)
Eq 19: Highly empirical. What would happen with different approaches? How sensitive is your model to these parameters? What about phosphorus limitation on Vcmax? See:
Walker, A.P., Beckerman, A.P., Gu, L., Kattge, J., Cernusak, L.A., Domingues, T.F., Scales, J.C., Wohlfahrt, G., Wullschleger, S.D., Woodward, F.I., 2014. The relationship of leaf photosynthetic traits - Vcmax and Jmax - to leaf nitrogen, leaf phosphorus, and specific leaf area: A meta-analysis and modeling study. Ecol. Evol. https://doi.org/10.1002/ece3.1173
Line 228: Remove Common Era. This isn’t necessary.
Figure 2: Although I appreciate the comparison with FLUXCOM GPP, I find it rather limited and it would be more insightful if other studies with P cycles on were added into the figure. Please see:
Wang, Y., Ciais, P., Goll, D., Huang, Y., Luo, Y., Wang, Y.P., Bloom, A.A., Broquet, G., Hartmann, J., Peng, S., Penuelas, J., Piao, S., Sardans, J., Stocker, B.D., Wang, R., Zaehle, S., Zechmeister-Boltenstern, S., 2018. GOLUM-CNP v1.0: A data-driven modeling of carbon, nitrogen and phosphorus cycles in major terrestrial biomes. Geosci. Model Dev. 11, 3903–3928. https://doi.org/10.5194/gmd-11-3903-2018
Goll, D.S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J., Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B., Zaehle, S., Penuelas, J., Janssens, I., Ciais, P., 2017. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. https://doi.org/10.5194/gmd-10-3745-2017
Braghiere, R.K., Fisher, J.B., Allen, K., Brzostek, E., Shi, M., Yang, X., Ricciuto, D.M., Fisher, R.A., Zhu, Q., Phillips, R.P., 2022. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. e2022MS003204. https://doi.org/10.1029/2022MS003204
The Baseline GPP of the model substantially overestimates FLUXCOM, why is that?
Line 274: NPP of 72 PgCyr also seems a bit high. What is a good estimate of global NPP? Add values in the study.
Figure 7. These values could be compared to other studies as well. See:
Braghiere, R.K., Fisher, J.B., Allen, K., Brzostek, E., Shi, M., Yang, X., Ricciuto, D.M., Fisher, R.A., Zhu, Q., Phillips, R.P., 2022. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. e2022MS003204. https://doi.org/10.1029/2022MS003204
Section 4: Limitations and applications of the terrestrial nutrient modules
I would add the role of mycorrhizae into NP acquisition. Please refer to:
Braghiere, R.K., Fisher, J.B., Fisher, R.A., Shi, M., Steidinger, B.S., Sulman, B.N., Soudzilovskaia, N.A., Yang, X., Liang, J., Peay, K.G., Crowther, T.W., Phillips, R.P., 2021. Mycorrhizal Distributions Impact Global Patterns of Carbon and Nutrient Cycling. Geophys. Res. Lett. 48. https://doi.org/10.1029/2021GL094514
Shi, M., Fisher, J.B., Brzostek, E.R., Phillips, R.P., 2016. Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model. Glob. Chang. Biol. 22, 1299–1314. https://doi.org/10.1111/gcb.13131
Citation: https://doi.org/10.5194/gmd-2022-191-RC3 - AC1: 'Response to reviewers', Makcim Luis De Sisto Lelchitskaya, 15 Mar 2023
Makcim L. De Sisto et al.
Makcim L. De Sisto et al.
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