Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Implications for the Future Terrestrial Carbon Sink

Kou-Giesbrecht, Sian; Arora, Vivek; Seiler, Christian; Arneth, Almut; Falk, Stefanie; Jain, Atul; Joos, Fortunat; Kennedy, Daniel; Knauer, Jürgen; Sitch, Stephen; O'Sullivan, Michael; Pan, Naiqing; Sun, Qing; Tian, Hanqin; Vuichard, Nicolas; Zaehle, Sönke

doi:https://doi.org/10.5194/egusphere-2023-167

Preprints

https://doi.org/10.5194/egusphere-2023-167

Preprints

06 Feb 2023

| 06 Feb 2023

Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Implications for the Future Terrestrial Carbon Sink

Sian Kou-Giesbrecht, Vivek Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle

Abstract. Terrestrial carbon (C) sequestration is limited by nitrogen (N), a constraint that could intensify under CO₂ fertilisation and future global change. The terrestrial C sink is estimated to currently sequester approximately a third of annual anthropogenic CO₂ emissions based on an ensemble of terrestrial biosphere models, which have been evaluated in their ability to reproduce observations of the C, water, and energy cycles. However, their ability to reproduce observations of N cycling and thus the regulation of terrestrial C sequestration by N has been largely unexplored. Here, we evaluate an ensemble of terrestrial biosphere models with coupled C-N cycling and their performance at simulating N cycling, outlining a framework for evaluating N cycling that can be applied across terrestrial biosphere models. We find that models exhibit significant variability across N pools and fluxes, simulating different magnitudes and trends over the historical period, despite their ability to generally reproduce the historical terrestrial C sink. This suggests that the underlying N processes that regulate terrestrial C sequestration operate differently across models and may not be fully captured. Furthermore, models tended to overestimate tropical biological N fixation, vegetation C:N ratio, and soil C:N ratio but underestimate temperate biological N fixation relative to observations. However, there is significant uncertainty associated with measurements of N cycling processes given their scarcity (especially relative to those of C cycling processes) and their high spatiotemporal variability. Overall, our results suggest that terrestrial biosphere models that represent coupled C-N cycling (let alone those without a representation of N cycling) could be overestimating C storage per unit N, which could lead to biases in projections of the future terrestrial C sink under CO₂ fertilisation and future global change. More extensive observations of N cycling processes are crucial to evaluate N cycling and its impact on C cycling as well as guide its development in terrestrial biosphere models.

Received: 03 Feb 2023 – Discussion started: 06 Feb 2023

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14 Aug 2023

Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles

Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle

Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023,https://doi.org/10.5194/esd-14-767-2023, 2023

Short summary

Sian Kou-Giesbrecht et al.

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2023-167', Joshua Fisher, 24 Feb 2023

Publisher’s note: this comment is a copy of RC1 and its content was therefore removed.

Citation: https://doi.org/10.5194/egusphere-2023-167-CC1
- AC1: 'Reply on RC1', Sian Kou-Giesbrecht, 16 Mar 2023
  
  First, we want to thank you for the positive comments on our manuscript!
  We agree that the main outcome is that the models exhibit significant variability across N pools and fluxes. We attempted to examine the mechanisms underlying this spread and uncertainty by analyzing the representations of four central N cycling processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. Similarly, while the implications of this spread and uncertainty are not within the scope of this work (given that the TRENDY simulations focus on the historical period), we conjecture that they are likely to influence future projections (see below). In our revised manuscript, we will reframe the text to highlight our main findings more clearly in the abstract and in the discussion section as well as the focus and constraints of our study. Furthermore, we agree that the title of the manuscript could be more descriptive and we will brainstorm a better title (perhaps, “Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Spread Across Models Suggests Uncertainty in Projections of the Future Terrestrial Carbon Sink”).
  You are completely correct that, although our results find that the ability of this suite of TBMs in reproducing the terrestrial C sink appears to be disconnected from the N cycle, the central purpose of the N cycle is to constrain CO2 fertilization which is not directly evaluated in our study. Evaluating the ability of a TBM to simulate present-day N cycling processes is one way to assess its ability to simulate N limitation of CO2 fertilization in the future albeit an indirect one. While not a direct test of the sensitivity of the C cycle to the N cycle, the significant variability across present-day N pools and fluxes that we assessed suggests that there could be uncertainty in future projections of the terrestrial C sink. We agree with you that a robust test of the response to CO2 fertilization and N fertilization across this ensemble of TBMs would be ideal for evaluation of the N cycle. In our revised manuscript, we will clarify this distinction and the limitations of our study. We will also emphasize the importance of experimental manipulations during future model evaluations and intercomparisons.
  Furthermore, as per your suggestion, we will add an additional assessment of the correlations between the bias in simulated BNF, vegetation C:N, and soil C:N and the bias in simulated NBP (as well as other C cycle variables). If this analysis yields interesting results, we will add it to the revised manuscript. In our original analysis, we assessed correlations between the overall scores for BNF, vegetation C:N, and soil C:N and the overall score for NBP (as well as other C cycle variables) in Figure A3. However, no significant correlations emerged which is likely due to the many structural differences between models that confound these relationships. In the revised manuscript, we will highlight this analysis more in the main text.
  With respect to the minor comments, we will update our explanation of BNF in CLM5. While Cawse-Nicholson et al. 2021 points to many useful products, they are only at the site level or at a regional scale. While Fisher et al. 2012 provides a global product, it is for nutrient limitation which, as discussed above, would require the simulation of nutrient fertilisation experiments in the TBMs. We considered a remote sensing product for leaf N at the global scale (Moreno-Martinez et al. 2018; doi:10.1016/j.rse.2018.09.006) but TRENDY model outputs are given as vegetation N rather than as leaf N. This necessitates an analysis of root and wood C:N and relative tissue allocation as we conducted in our study. We will include references to these remote sensing studies in the revised manuscript as they are useful resources for future work. We will also include reference to the energy exascale Earth system (E3SM) land model (ELM) (Braghiere et al. 2022). While not part of the TRENDY ensemble, ELM is an important example of a TBM with both N and P cycling.
  
  Citation: https://doi.org/10.5194/egusphere-2023-167-AC1
RC1:
'Comment on egusphere-2023-167', Joshua Fisher, 27 Feb 2023

This is a well-written assessment of modeled nitrogen cycle outputs from the latest TRENDY. Overall, it’s a good reference in documenting these outputs and the figures are good. The paper reads more like documentation than process-level science advance, but I think that’s fine for EGUsphere. As often with these analyses, the outcomes are that there is model spread and uncertainty, but the mechanisms and implications are somewhat opaque. Although, the science advance could be gleaned from the alarming statements (see below) scattered throughout as a bit of a call to arms. Perhaps change the paper title to be more impactful / less vague after the colon to reflect some of these alarming findings.

The striking statements to me included those that indicated no difference in C sink with and without N cycling; no score differences among models with different representations of BNF, N limitation to growth, decomposition, etc.; and, models generally reproduced the historical C sink despite huge variability in N pools/fluxes, and other seemingly glaring issues like constant soil C:N.

These statements are alarming and disconnect from the authors’ statements that N cycling should be important; but, the statements made by the authors above suggest otherwise—N cycling is unimportant, as the models will do whatever they do seemingly disconnected from N cycling (e.g., they’re tuned to the C sink).

So, this makes me wonder 2 things: 1) is the N cycle really just totally decoupled from the C cycle in the models; and/or, 2) are the authors performing the right tests to understand the sensitivity of the C cycle from the N cycle. For #2, the authors focus on BNF, and Veg and Soil C:N. It seems that the tests need to go another step further in some sort of normalization into NBP. The tests as presented seem indirect, but then the authors make bold statements about the importance, making it hard to trace the justification. Were there tests on progressive N limitation? Tipping points? Issues with N fertilization? Etc. If there is constant soil C:N, shouldn’t this manifest somewhere problematic in the C sink?

Minor comments:
L180. CLM5.0 increases BNG with N limitation only if there’s enough C to pay for it [Fisher et al., 2010; Shi et al., 2016; Fisher et al., 2019].

L578. Could be that new hyperspectral remote sensing could provide a nice constraint on Canopy N (e.g., SBG) [Cawse-Nicholson et al., 2021]. See also product from Fisher et al. [2012].

L581. See also Braghiere et al. [2022].

Good work overall!
Josh Fisher

References
Braghiere, R., J. Fisher, K. Allen, E. Brzostek, M. Shi, X. Yang, D. Ricciuto, R. Fisher, Q. Zhu, and R. Phillips (2022), Modeling global carbon costs of plant nitrogen and phosphorus acquisition, Journal of Advances in Modeling Earth Systems, 14(8), e2022MS003204.
Cawse-Nicholson, K., et al. (2021), NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms, Remote Sensing of Environment, 257, 112349.
Fisher, J. B., G. Badgley, and E. Blyth (2012), Global nutrient limitation in terrestrial vegetation, Global Biogeochemical Cycles, 26(3), GB3007.
Fisher, J. B., S. Sitch, Y. Malhi, R. A. Fisher, C. Huntingford, and S.-Y. Tan (2010), Carbon cost of plant nitrogen acquisition: A mechanistic, globally-applicable model of plant nitrogen uptake and fixation, Global Biogeochemical Cycles, 24(GB1014), doi:10.1029/2009GB003621.
Fisher, R. A., W. R. Wieder, B. M. Sanderson, C. D. Koven, K. W. Oleson, C. Xu, J. B. Fisher, M. Shi, A. P. Walker, and D. M. Lawrence (2019), Parametric controls on vegetation responses to biogeochemical forcing in the CLM5, Journal of Advances in Modeling Earth Systems, 11(9), 2879-2895.
Shi, M., J. B. Fisher, E. R. Brzostek, and R. P. Phillips (2016), Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model, Global Change Biology, 22(3), 1299-1314.

Citation: https://doi.org/10.5194/egusphere-2023-167-RC1
- AC1:
  'Reply on RC1', Sian Kou-Giesbrecht, 16 Mar 2023
  
  First, we want to thank you for the positive comments on our manuscript!
  We agree that the main outcome is that the models exhibit significant variability across N pools and fluxes. We attempted to examine the mechanisms underlying this spread and uncertainty by analyzing the representations of four central N cycling processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. Similarly, while the implications of this spread and uncertainty are not within the scope of this work (given that the TRENDY simulations focus on the historical period), we conjecture that they are likely to influence future projections (see below). In our revised manuscript, we will reframe the text to highlight our main findings more clearly in the abstract and in the discussion section as well as the focus and constraints of our study. Furthermore, we agree that the title of the manuscript could be more descriptive and we will brainstorm a better title (perhaps, “Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Spread Across Models Suggests Uncertainty in Projections of the Future Terrestrial Carbon Sink”).
  You are completely correct that, although our results find that the ability of this suite of TBMs in reproducing the terrestrial C sink appears to be disconnected from the N cycle, the central purpose of the N cycle is to constrain CO2 fertilization which is not directly evaluated in our study. Evaluating the ability of a TBM to simulate present-day N cycling processes is one way to assess its ability to simulate N limitation of CO2 fertilization in the future albeit an indirect one. While not a direct test of the sensitivity of the C cycle to the N cycle, the significant variability across present-day N pools and fluxes that we assessed suggests that there could be uncertainty in future projections of the terrestrial C sink. We agree with you that a robust test of the response to CO2 fertilization and N fertilization across this ensemble of TBMs would be ideal for evaluation of the N cycle. In our revised manuscript, we will clarify this distinction and the limitations of our study. We will also emphasize the importance of experimental manipulations during future model evaluations and intercomparisons.
  Furthermore, as per your suggestion, we will add an additional assessment of the correlations between the bias in simulated BNF, vegetation C:N, and soil C:N and the bias in simulated NBP (as well as other C cycle variables). If this analysis yields interesting results, we will add it to the revised manuscript. In our original analysis, we assessed correlations between the overall scores for BNF, vegetation C:N, and soil C:N and the overall score for NBP (as well as other C cycle variables) in Figure A3. However, no significant correlations emerged which is likely due to the many structural differences between models that confound these relationships. In the revised manuscript, we will highlight this analysis more in the main text.
  With respect to the minor comments, we will update our explanation of BNF in CLM5. While Cawse-Nicholson et al. 2021 points to many useful products, they are only at the site level or at a regional scale. While Fisher et al. 2012 provides a global product, it is for nutrient limitation which, as discussed above, would require the simulation of nutrient fertilisation experiments in the TBMs. We considered a remote sensing product for leaf N at the global scale (Moreno-Martinez et al. 2018; doi:10.1016/j.rse.2018.09.006) but TRENDY model outputs are given as vegetation N rather than as leaf N. This necessitates an analysis of root and wood C:N and relative tissue allocation as we conducted in our study. We will include references to these remote sensing studies in the revised manuscript as they are useful resources for future work. We will also include reference to the energy exascale Earth system (E3SM) land model (ELM) (Braghiere et al. 2022). While not part of the TRENDY ensemble, ELM is an important example of a TBM with both N and P cycling.
  
  Citation: https://doi.org/10.5194/egusphere-2023-167-AC1
  - RC2: 'Reply on AC1', Joshua Fisher, 16 Mar 2023
    
    Sounds good. Perhaps the post-colon title could be “…: the terrestrial C sink appears to be disconnected from the N cycle”… Not sure if you’re ready to go down that inflammatory path at this stage in your early career, but it would add fuel to the conversation!
    Just as a FYI, not related to the core of your paper, Braghiere et al. used the Nutrient Limitation product to evaluate global modeling sans fertilization experiments. Second, most of a plant’s N is in the canopy so one could still use canopy N data for evaluation (with caveats).
    
    Citation: https://doi.org/10.5194/egusphere-2023-167-RC2
    
    AC1: 'Reply on RC1', Sian Kou-Giesbrecht, 16 Mar 2023
    
    First, we want to thank you for the positive comments on our manuscript!
    We agree that the main outcome is that the models exhibit significant variability across N pools and fluxes. We attempted to examine the mechanisms underlying this spread and uncertainty by analyzing the representations of four central N cycling processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. Similarly, while the implications of this spread and uncertainty are not within the scope of this work (given that the TRENDY simulations focus on the historical period), we conjecture that they are likely to influence future projections (see below). In our revised manuscript, we will reframe the text to highlight our main findings more clearly in the abstract and in the discussion section as well as the focus and constraints of our study. Furthermore, we agree that the title of the manuscript could be more descriptive and we will brainstorm a better title (perhaps, “Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Spread Across Models Suggests Uncertainty in Projections of the Future Terrestrial Carbon Sink”).
    You are completely correct that, although our results find that the ability of this suite of TBMs in reproducing the terrestrial C sink appears to be disconnected from the N cycle, the central purpose of the N cycle is to constrain CO2 fertilization which is not directly evaluated in our study. Evaluating the ability of a TBM to simulate present-day N cycling processes is one way to assess its ability to simulate N limitation of CO2 fertilization in the future albeit an indirect one. While not a direct test of the sensitivity of the C cycle to the N cycle, the significant variability across present-day N pools and fluxes that we assessed suggests that there could be uncertainty in future projections of the terrestrial C sink. We agree with you that a robust test of the response to CO2 fertilization and N fertilization across this ensemble of TBMs would be ideal for evaluation of the N cycle. In our revised manuscript, we will clarify this distinction and the limitations of our study. We will also emphasize the importance of experimental manipulations during future model evaluations and intercomparisons.
    Furthermore, as per your suggestion, we will add an additional assessment of the correlations between the bias in simulated BNF, vegetation C:N, and soil C:N and the bias in simulated NBP (as well as other C cycle variables). If this analysis yields interesting results, we will add it to the revised manuscript. In our original analysis, we assessed correlations between the overall scores for BNF, vegetation C:N, and soil C:N and the overall score for NBP (as well as other C cycle variables) in Figure A3. However, no significant correlations emerged which is likely due to the many structural differences between models that confound these relationships. In the revised manuscript, we will highlight this analysis more in the main text.
    With respect to the minor comments, we will update our explanation of BNF in CLM5. While Cawse-Nicholson et al. 2021 points to many useful products, they are only at the site level or at a regional scale. While Fisher et al. 2012 provides a global product, it is for nutrient limitation which, as discussed above, would require the simulation of nutrient fertilisation experiments in the TBMs. We considered a remote sensing product for leaf N at the global scale (Moreno-Martinez et al. 2018; doi:10.1016/j.rse.2018.09.006) but TRENDY model outputs are given as vegetation N rather than as leaf N. This necessitates an analysis of root and wood C:N and relative tissue allocation as we conducted in our study. We will include references to these remote sensing studies in the revised manuscript as they are useful resources for future work. We will also include reference to the energy exascale Earth system (E3SM) land model (ELM) (Braghiere et al. 2022). While not part of the TRENDY ensemble, ELM is an important example of a TBM with both N and P cycling.
    
    Citation: https://doi.org/10.5194/egusphere-2023-167-AC1
RC3:
'Comment on egusphere-2023-167', Anonymous Referee #2, 22 Apr 2023
Review of "Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Implications for the Future Terrestrial Carbon Sink"

The paper evaluates 11 TRENDY models for simulating nitrogen cycle processes and compares them in detail with different available datasets. The paper is very well structured, well written with a relevant and topical theme. The study does a great job in comparing the different models and the analysis is comprehensive and statistically sound.
However, the paper fails to provide an overall understanding of the importance of nitrogen cycle in estimating carbon fluxes given the estimates it gives. Since the importance of nitrogen in the carbon cycle processes has been highlighted in the text in multiple places, statements that contradict this are also a part of the text. This leaves the reader puzzled with two main questions:
If the different TRENDY models (with no or some/different N cycle processes built in) can estimate C fluxes within a certain (acceptable) range despite estimating N fluxes that have a wide range of magnitude, what is the significance of integrating N cycle processes in the models? This has not been answered or proved anywhere in the text and is one of the major drawbacks of the paper.

Different models have implemented N cycle processes in different ways. The study could have made a prominent impact if it was able to identify which specific N cycle processes are crucial to implement in the models so that the N fluxes and pool estimates are comprehensively represented in the models and the resulting N pools and fluxes are better correlated to the observed datasets. This was probably a low hanging fruit for this study and could have been a significant contribution of the paper.

Some minor points:
Fig 2b.): NBP estimates from CarboScope and CAMS vary a lot from other datasets/observations in different range of latitudes. Please add an explanation in the text for this difference in observed datasets.

Fig 4: The plots represent average values of different N pools from the models. It would be helpful to add another set of similar plots with uncertainty ranges for estimates so that we can identify if there are specific regions where the estimates from different models are in a similar range and regions where the models produce very different numbers.

The Discussion and Conclusions sections should be edited to add the potential directions of future research, given the extensive analysis shown in the paper, in a less vague manner.

Overall, it’s a good publication that summarizes the current state of the nitrogen cycle in state-of-the-art TRENDY models. Good effort by the authors!
Citation: https://doi.org/10.5194/egusphere-2023-167-RC3
- AC2:
  'Reply on RC3', Sian Kou-Giesbrecht, 12 May 2023
  Thank you for your positive review of our manuscript!
  
  We agree that the major finding of our paper is that, despite the empirically established importance of N limitation of vegetation growth and CO2 fertilisation as well as its implementation in these terrestrial biosphere models (see Table 1), the ability of these models to reproduce the terrestrial C sink appears to be disconnected from the N cycle. Essentially, all models are calibrated to reproduce the behaviour of the C cycle and the terrestrial C sink over the historical period regardless of whether they represent N cycling or not. As such, although N cycling is implemented in these models, it does not have a consistent influence on C cycling across models. While this does not have a significant influence on historical simulations, this has important implications for future simulations of the terrestrial C sink (see Kou-Giesbrecht and Arora Geophysical Research Letters 2023). Our study’s main goal is to identify this important disconnect between C and N cycling and to encourage further investigation. As we discussed in our response to reviewer #1, our study does not directly evaluate how N constrains CO2 fertilisation because we only evaluated historical simulations that were performed for the Global Carbon Project (GCP). A robust test of the response to CO2 fertilisation (and N fertilisation) across these models would be ideal for evaluation of the N cycle but such simulations are not performed within the GCP.
  In the revised version of the manuscript we will first outline the empirically established importance of N limitation. Then, we will explain how, despite N limitation being implemented across models, a disconnect between C and N cycling occurs because models are calibrated to reproduce C cycling over the historical period. We will also emphasize the importance of experimental manipulations (such as CO2 and N fertilisation) for comprehensive model evaluations and intercomparisons.
  In our study, one of our aims was to identify how differences between representations of important N cycle processes are crucial to implementing N cycling. We identified four central N processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. We explained how different models represent these central N processes and attempted to distinguish significant differences between models in their abilities to reproduce both C and N cycling datasets. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. We will emphasise this in the discussion section of our revised manuscript.
  
  Minor points:
  Thank you for pointing out that this was unclear. We will add an explanation of the differences between datasets.
  
  We will add a figure showing the variability of the geographical distributions of N cycle processes across models to the supplement.
  
  We will add more concrete future research directions to the discussion section (as described in our response above).
  
  Citation: https://doi.org/10.5194/egusphere-2023-167-AC2

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2023-167', Joshua Fisher, 24 Feb 2023

Publisher’s note: this comment is a copy of RC1 and its content was therefore removed.

Citation: https://doi.org/10.5194/egusphere-2023-167-CC1
- AC1: 'Reply on RC1', Sian Kou-Giesbrecht, 16 Mar 2023
  
  First, we want to thank you for the positive comments on our manuscript!
  We agree that the main outcome is that the models exhibit significant variability across N pools and fluxes. We attempted to examine the mechanisms underlying this spread and uncertainty by analyzing the representations of four central N cycling processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. Similarly, while the implications of this spread and uncertainty are not within the scope of this work (given that the TRENDY simulations focus on the historical period), we conjecture that they are likely to influence future projections (see below). In our revised manuscript, we will reframe the text to highlight our main findings more clearly in the abstract and in the discussion section as well as the focus and constraints of our study. Furthermore, we agree that the title of the manuscript could be more descriptive and we will brainstorm a better title (perhaps, “Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Spread Across Models Suggests Uncertainty in Projections of the Future Terrestrial Carbon Sink”).
  You are completely correct that, although our results find that the ability of this suite of TBMs in reproducing the terrestrial C sink appears to be disconnected from the N cycle, the central purpose of the N cycle is to constrain CO2 fertilization which is not directly evaluated in our study. Evaluating the ability of a TBM to simulate present-day N cycling processes is one way to assess its ability to simulate N limitation of CO2 fertilization in the future albeit an indirect one. While not a direct test of the sensitivity of the C cycle to the N cycle, the significant variability across present-day N pools and fluxes that we assessed suggests that there could be uncertainty in future projections of the terrestrial C sink. We agree with you that a robust test of the response to CO2 fertilization and N fertilization across this ensemble of TBMs would be ideal for evaluation of the N cycle. In our revised manuscript, we will clarify this distinction and the limitations of our study. We will also emphasize the importance of experimental manipulations during future model evaluations and intercomparisons.
  Furthermore, as per your suggestion, we will add an additional assessment of the correlations between the bias in simulated BNF, vegetation C:N, and soil C:N and the bias in simulated NBP (as well as other C cycle variables). If this analysis yields interesting results, we will add it to the revised manuscript. In our original analysis, we assessed correlations between the overall scores for BNF, vegetation C:N, and soil C:N and the overall score for NBP (as well as other C cycle variables) in Figure A3. However, no significant correlations emerged which is likely due to the many structural differences between models that confound these relationships. In the revised manuscript, we will highlight this analysis more in the main text.
  With respect to the minor comments, we will update our explanation of BNF in CLM5. While Cawse-Nicholson et al. 2021 points to many useful products, they are only at the site level or at a regional scale. While Fisher et al. 2012 provides a global product, it is for nutrient limitation which, as discussed above, would require the simulation of nutrient fertilisation experiments in the TBMs. We considered a remote sensing product for leaf N at the global scale (Moreno-Martinez et al. 2018; doi:10.1016/j.rse.2018.09.006) but TRENDY model outputs are given as vegetation N rather than as leaf N. This necessitates an analysis of root and wood C:N and relative tissue allocation as we conducted in our study. We will include references to these remote sensing studies in the revised manuscript as they are useful resources for future work. We will also include reference to the energy exascale Earth system (E3SM) land model (ELM) (Braghiere et al. 2022). While not part of the TRENDY ensemble, ELM is an important example of a TBM with both N and P cycling.
  
  Citation: https://doi.org/10.5194/egusphere-2023-167-AC1
RC1:
'Comment on egusphere-2023-167', Joshua Fisher, 27 Feb 2023

This is a well-written assessment of modeled nitrogen cycle outputs from the latest TRENDY. Overall, it’s a good reference in documenting these outputs and the figures are good. The paper reads more like documentation than process-level science advance, but I think that’s fine for EGUsphere. As often with these analyses, the outcomes are that there is model spread and uncertainty, but the mechanisms and implications are somewhat opaque. Although, the science advance could be gleaned from the alarming statements (see below) scattered throughout as a bit of a call to arms. Perhaps change the paper title to be more impactful / less vague after the colon to reflect some of these alarming findings.

The striking statements to me included those that indicated no difference in C sink with and without N cycling; no score differences among models with different representations of BNF, N limitation to growth, decomposition, etc.; and, models generally reproduced the historical C sink despite huge variability in N pools/fluxes, and other seemingly glaring issues like constant soil C:N.

These statements are alarming and disconnect from the authors’ statements that N cycling should be important; but, the statements made by the authors above suggest otherwise—N cycling is unimportant, as the models will do whatever they do seemingly disconnected from N cycling (e.g., they’re tuned to the C sink).

So, this makes me wonder 2 things: 1) is the N cycle really just totally decoupled from the C cycle in the models; and/or, 2) are the authors performing the right tests to understand the sensitivity of the C cycle from the N cycle. For #2, the authors focus on BNF, and Veg and Soil C:N. It seems that the tests need to go another step further in some sort of normalization into NBP. The tests as presented seem indirect, but then the authors make bold statements about the importance, making it hard to trace the justification. Were there tests on progressive N limitation? Tipping points? Issues with N fertilization? Etc. If there is constant soil C:N, shouldn’t this manifest somewhere problematic in the C sink?

Minor comments:
L180. CLM5.0 increases BNG with N limitation only if there’s enough C to pay for it [Fisher et al., 2010; Shi et al., 2016; Fisher et al., 2019].

L578. Could be that new hyperspectral remote sensing could provide a nice constraint on Canopy N (e.g., SBG) [Cawse-Nicholson et al., 2021]. See also product from Fisher et al. [2012].

L581. See also Braghiere et al. [2022].

Good work overall!
Josh Fisher

References
Braghiere, R., J. Fisher, K. Allen, E. Brzostek, M. Shi, X. Yang, D. Ricciuto, R. Fisher, Q. Zhu, and R. Phillips (2022), Modeling global carbon costs of plant nitrogen and phosphorus acquisition, Journal of Advances in Modeling Earth Systems, 14(8), e2022MS003204.
Cawse-Nicholson, K., et al. (2021), NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms, Remote Sensing of Environment, 257, 112349.
Fisher, J. B., G. Badgley, and E. Blyth (2012), Global nutrient limitation in terrestrial vegetation, Global Biogeochemical Cycles, 26(3), GB3007.
Fisher, J. B., S. Sitch, Y. Malhi, R. A. Fisher, C. Huntingford, and S.-Y. Tan (2010), Carbon cost of plant nitrogen acquisition: A mechanistic, globally-applicable model of plant nitrogen uptake and fixation, Global Biogeochemical Cycles, 24(GB1014), doi:10.1029/2009GB003621.
Fisher, R. A., W. R. Wieder, B. M. Sanderson, C. D. Koven, K. W. Oleson, C. Xu, J. B. Fisher, M. Shi, A. P. Walker, and D. M. Lawrence (2019), Parametric controls on vegetation responses to biogeochemical forcing in the CLM5, Journal of Advances in Modeling Earth Systems, 11(9), 2879-2895.
Shi, M., J. B. Fisher, E. R. Brzostek, and R. P. Phillips (2016), Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model, Global Change Biology, 22(3), 1299-1314.

Citation: https://doi.org/10.5194/egusphere-2023-167-RC1
- AC1:
  'Reply on RC1', Sian Kou-Giesbrecht, 16 Mar 2023
  
  First, we want to thank you for the positive comments on our manuscript!
  We agree that the main outcome is that the models exhibit significant variability across N pools and fluxes. We attempted to examine the mechanisms underlying this spread and uncertainty by analyzing the representations of four central N cycling processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. Similarly, while the implications of this spread and uncertainty are not within the scope of this work (given that the TRENDY simulations focus on the historical period), we conjecture that they are likely to influence future projections (see below). In our revised manuscript, we will reframe the text to highlight our main findings more clearly in the abstract and in the discussion section as well as the focus and constraints of our study. Furthermore, we agree that the title of the manuscript could be more descriptive and we will brainstorm a better title (perhaps, “Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Spread Across Models Suggests Uncertainty in Projections of the Future Terrestrial Carbon Sink”).
  You are completely correct that, although our results find that the ability of this suite of TBMs in reproducing the terrestrial C sink appears to be disconnected from the N cycle, the central purpose of the N cycle is to constrain CO2 fertilization which is not directly evaluated in our study. Evaluating the ability of a TBM to simulate present-day N cycling processes is one way to assess its ability to simulate N limitation of CO2 fertilization in the future albeit an indirect one. While not a direct test of the sensitivity of the C cycle to the N cycle, the significant variability across present-day N pools and fluxes that we assessed suggests that there could be uncertainty in future projections of the terrestrial C sink. We agree with you that a robust test of the response to CO2 fertilization and N fertilization across this ensemble of TBMs would be ideal for evaluation of the N cycle. In our revised manuscript, we will clarify this distinction and the limitations of our study. We will also emphasize the importance of experimental manipulations during future model evaluations and intercomparisons.
  Furthermore, as per your suggestion, we will add an additional assessment of the correlations between the bias in simulated BNF, vegetation C:N, and soil C:N and the bias in simulated NBP (as well as other C cycle variables). If this analysis yields interesting results, we will add it to the revised manuscript. In our original analysis, we assessed correlations between the overall scores for BNF, vegetation C:N, and soil C:N and the overall score for NBP (as well as other C cycle variables) in Figure A3. However, no significant correlations emerged which is likely due to the many structural differences between models that confound these relationships. In the revised manuscript, we will highlight this analysis more in the main text.
  With respect to the minor comments, we will update our explanation of BNF in CLM5. While Cawse-Nicholson et al. 2021 points to many useful products, they are only at the site level or at a regional scale. While Fisher et al. 2012 provides a global product, it is for nutrient limitation which, as discussed above, would require the simulation of nutrient fertilisation experiments in the TBMs. We considered a remote sensing product for leaf N at the global scale (Moreno-Martinez et al. 2018; doi:10.1016/j.rse.2018.09.006) but TRENDY model outputs are given as vegetation N rather than as leaf N. This necessitates an analysis of root and wood C:N and relative tissue allocation as we conducted in our study. We will include references to these remote sensing studies in the revised manuscript as they are useful resources for future work. We will also include reference to the energy exascale Earth system (E3SM) land model (ELM) (Braghiere et al. 2022). While not part of the TRENDY ensemble, ELM is an important example of a TBM with both N and P cycling.
  
  Citation: https://doi.org/10.5194/egusphere-2023-167-AC1
  - RC2: 'Reply on AC1', Joshua Fisher, 16 Mar 2023
    
    Sounds good. Perhaps the post-colon title could be “…: the terrestrial C sink appears to be disconnected from the N cycle”… Not sure if you’re ready to go down that inflammatory path at this stage in your early career, but it would add fuel to the conversation!
    Just as a FYI, not related to the core of your paper, Braghiere et al. used the Nutrient Limitation product to evaluate global modeling sans fertilization experiments. Second, most of a plant’s N is in the canopy so one could still use canopy N data for evaluation (with caveats).
    
    Citation: https://doi.org/10.5194/egusphere-2023-167-RC2
    
    AC1: 'Reply on RC1', Sian Kou-Giesbrecht, 16 Mar 2023
    
    First, we want to thank you for the positive comments on our manuscript!
    We agree that the main outcome is that the models exhibit significant variability across N pools and fluxes. We attempted to examine the mechanisms underlying this spread and uncertainty by analyzing the representations of four central N cycling processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. Similarly, while the implications of this spread and uncertainty are not within the scope of this work (given that the TRENDY simulations focus on the historical period), we conjecture that they are likely to influence future projections (see below). In our revised manuscript, we will reframe the text to highlight our main findings more clearly in the abstract and in the discussion section as well as the focus and constraints of our study. Furthermore, we agree that the title of the manuscript could be more descriptive and we will brainstorm a better title (perhaps, “Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Spread Across Models Suggests Uncertainty in Projections of the Future Terrestrial Carbon Sink”).
    You are completely correct that, although our results find that the ability of this suite of TBMs in reproducing the terrestrial C sink appears to be disconnected from the N cycle, the central purpose of the N cycle is to constrain CO2 fertilization which is not directly evaluated in our study. Evaluating the ability of a TBM to simulate present-day N cycling processes is one way to assess its ability to simulate N limitation of CO2 fertilization in the future albeit an indirect one. While not a direct test of the sensitivity of the C cycle to the N cycle, the significant variability across present-day N pools and fluxes that we assessed suggests that there could be uncertainty in future projections of the terrestrial C sink. We agree with you that a robust test of the response to CO2 fertilization and N fertilization across this ensemble of TBMs would be ideal for evaluation of the N cycle. In our revised manuscript, we will clarify this distinction and the limitations of our study. We will also emphasize the importance of experimental manipulations during future model evaluations and intercomparisons.
    Furthermore, as per your suggestion, we will add an additional assessment of the correlations between the bias in simulated BNF, vegetation C:N, and soil C:N and the bias in simulated NBP (as well as other C cycle variables). If this analysis yields interesting results, we will add it to the revised manuscript. In our original analysis, we assessed correlations between the overall scores for BNF, vegetation C:N, and soil C:N and the overall score for NBP (as well as other C cycle variables) in Figure A3. However, no significant correlations emerged which is likely due to the many structural differences between models that confound these relationships. In the revised manuscript, we will highlight this analysis more in the main text.
    With respect to the minor comments, we will update our explanation of BNF in CLM5. While Cawse-Nicholson et al. 2021 points to many useful products, they are only at the site level or at a regional scale. While Fisher et al. 2012 provides a global product, it is for nutrient limitation which, as discussed above, would require the simulation of nutrient fertilisation experiments in the TBMs. We considered a remote sensing product for leaf N at the global scale (Moreno-Martinez et al. 2018; doi:10.1016/j.rse.2018.09.006) but TRENDY model outputs are given as vegetation N rather than as leaf N. This necessitates an analysis of root and wood C:N and relative tissue allocation as we conducted in our study. We will include references to these remote sensing studies in the revised manuscript as they are useful resources for future work. We will also include reference to the energy exascale Earth system (E3SM) land model (ELM) (Braghiere et al. 2022). While not part of the TRENDY ensemble, ELM is an important example of a TBM with both N and P cycling.
    
    Citation: https://doi.org/10.5194/egusphere-2023-167-AC1
RC3:
'Comment on egusphere-2023-167', Anonymous Referee #2, 22 Apr 2023
Review of "Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Implications for the Future Terrestrial Carbon Sink"

The paper evaluates 11 TRENDY models for simulating nitrogen cycle processes and compares them in detail with different available datasets. The paper is very well structured, well written with a relevant and topical theme. The study does a great job in comparing the different models and the analysis is comprehensive and statistically sound.
However, the paper fails to provide an overall understanding of the importance of nitrogen cycle in estimating carbon fluxes given the estimates it gives. Since the importance of nitrogen in the carbon cycle processes has been highlighted in the text in multiple places, statements that contradict this are also a part of the text. This leaves the reader puzzled with two main questions:
If the different TRENDY models (with no or some/different N cycle processes built in) can estimate C fluxes within a certain (acceptable) range despite estimating N fluxes that have a wide range of magnitude, what is the significance of integrating N cycle processes in the models? This has not been answered or proved anywhere in the text and is one of the major drawbacks of the paper.

Different models have implemented N cycle processes in different ways. The study could have made a prominent impact if it was able to identify which specific N cycle processes are crucial to implement in the models so that the N fluxes and pool estimates are comprehensively represented in the models and the resulting N pools and fluxes are better correlated to the observed datasets. This was probably a low hanging fruit for this study and could have been a significant contribution of the paper.

Some minor points:
Fig 2b.): NBP estimates from CarboScope and CAMS vary a lot from other datasets/observations in different range of latitudes. Please add an explanation in the text for this difference in observed datasets.

Fig 4: The plots represent average values of different N pools from the models. It would be helpful to add another set of similar plots with uncertainty ranges for estimates so that we can identify if there are specific regions where the estimates from different models are in a similar range and regions where the models produce very different numbers.

The Discussion and Conclusions sections should be edited to add the potential directions of future research, given the extensive analysis shown in the paper, in a less vague manner.

Overall, it’s a good publication that summarizes the current state of the nitrogen cycle in state-of-the-art TRENDY models. Good effort by the authors!
Citation: https://doi.org/10.5194/egusphere-2023-167-RC3
- AC2:
  'Reply on RC3', Sian Kou-Giesbrecht, 12 May 2023
  Thank you for your positive review of our manuscript!
  
  We agree that the major finding of our paper is that, despite the empirically established importance of N limitation of vegetation growth and CO2 fertilisation as well as its implementation in these terrestrial biosphere models (see Table 1), the ability of these models to reproduce the terrestrial C sink appears to be disconnected from the N cycle. Essentially, all models are calibrated to reproduce the behaviour of the C cycle and the terrestrial C sink over the historical period regardless of whether they represent N cycling or not. As such, although N cycling is implemented in these models, it does not have a consistent influence on C cycling across models. While this does not have a significant influence on historical simulations, this has important implications for future simulations of the terrestrial C sink (see Kou-Giesbrecht and Arora Geophysical Research Letters 2023). Our study’s main goal is to identify this important disconnect between C and N cycling and to encourage further investigation. As we discussed in our response to reviewer #1, our study does not directly evaluate how N constrains CO2 fertilisation because we only evaluated historical simulations that were performed for the Global Carbon Project (GCP). A robust test of the response to CO2 fertilisation (and N fertilisation) across these models would be ideal for evaluation of the N cycle but such simulations are not performed within the GCP.
  In the revised version of the manuscript we will first outline the empirically established importance of N limitation. Then, we will explain how, despite N limitation being implemented across models, a disconnect between C and N cycling occurs because models are calibrated to reproduce C cycling over the historical period. We will also emphasize the importance of experimental manipulations (such as CO2 and N fertilisation) for comprehensive model evaluations and intercomparisons.
  In our study, one of our aims was to identify how differences between representations of important N cycle processes are crucial to implementing N cycling. We identified four central N processes: N limitation of vegetation growth, biological N fixation, vegetation response to N limitation, and N limitation of decomposition. We explained how different models represent these central N processes and attempted to distinguish significant differences between models in their abilities to reproduce both C and N cycling datasets. However, no significant differences between models with different representations of these processes emerged. This is likely due to other structural differences between models that confound their effects (an issue in all model intercomparisons). Further analysis is clearly warranted in future work. We will emphasise this in the discussion section of our revised manuscript.
  
  Minor points:
  Thank you for pointing out that this was unclear. We will add an explanation of the differences between datasets.
  
  We will add a figure showing the variability of the geographical distributions of N cycle processes across models to the supplement.
  
  We will add more concrete future research directions to the discussion section (as described in our response above).
  
  Citation: https://doi.org/10.5194/egusphere-2023-167-AC2

Peer review completion

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

ED: Reconsider after major revisions (30 May 2023) by Somnath Baidya Roy

AR by Sian Kou-Giesbrecht on behalf of the Authors (07 Jul 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (11 Jul 2023) by Somnath Baidya Roy

RR by Joshua Fisher (11 Jul 2023)

ED: Publish as is (12 Jul 2023) by Somnath Baidya Roy

AR by Sian Kou-Giesbrecht on behalf of the Authors (13 Jul 2023) Manuscript

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14 Aug 2023

Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles

Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023,https://doi.org/10.5194/esd-14-767-2023, 2023

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Sian Kou-Giesbrecht et al.

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Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that they simulate significant variability in N processes. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain and more are necessary to guide the development of N cycling in models.


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Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Implications for the Future Terrestrial Carbon Sink

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