the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Nov 2022
A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model, SSiB version5/TRIFFID/DayCent-SOM: Its parameterization, implementation, and evaluation
Abstract. Plant and microbial nitrogen (N) dynamics and N availability regulate the photosynthetic capacity and capture, allocation, and turnover of carbon (C) in terrestrial ecosystems. Studies have shown that a wide divergence in representations of N dynamics in terrestrial surface processes leads to large uncertainty in climate simulations and the projections of future trajectories. In this study, a plant C-N interface coupling framework was developed and implemented in a coupled biophysical-ecosystem-biogeochemical model (SSiB5/TRIFFID/DayCent-SOM). The main concept and structure of this plant C-N framework and its coupling methodology are presented. Different from many current approaches, this framework not only involves soil organic matter cycling, but uniquely takes into account plant N metabolism first, such as plant resistance and self-adjustment, which are represented by dynamic C / N ratios for each plant functional type (PFT). Then, when available N is less than plant N demand, N restricts plant growth, reducing gross primary productivity (GPP), and modulating plant respiration rates and phenology. All these considerations ensure a full incorporation of N regulations to plant growth and C cycling. This new approach has been tested to assess the effects of this coupling framework and N limitation on the terrestrial carbon cycle. Measurements from flux tower sites with different PFTs from 1996–2013 and global satellite-derived observations from 1948–2007 are used as references to assess the effect of the C-N coupling process on the long-term mean vegetation distribution and terrestrial C cycling using the offline SSiB5/TRIFFID/DayCent-SOM model, which use observed meteorological forcing to drive the model. The sensitivity of the terrestrial C cycle to different components in the framework is also assessed. The results show a general improvement with the new plant C-N coupling framework, with more consistent emergent properties, such as GPP, leaf area index (LAI), and respiration, compared to observations. The main improvements occur in tropical Africa and boreal regions, accompanied by a decrease of the bias in global GPP and LAI by 16.3 % and 27.1 %, respectively.
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RC1: 'Comment on egusphere-2022-1111', Anonymous Referee #1, 04 Jan 2023
This work coupled the nitrogen model DayCent-SOM with the land surface moodel SSiB5/TRIFFID to study the N limitation on GPP,NPP and LAI. It is interesting to implement the carbon-nitrogen sutdy, which can be useful to earth system models or climate system models and it might improve the land-air interaction. The simulation with nitrogen shows the general better performance compared to that wihout N processes of SSiB4. But in some regions, such as Amazon and eastern China, the incorpoation of N limitation leads to larger underestimation of carbon fluxes, which is needed to more discussion the mechanism in science.
Citation: https://doi.org/10.5194/egusphere-2022-1111-RC1 -
AC1: 'Reply on RC1', Zheng Xiang, 19 Mar 2023
This work coupled the nitrogen model DayCent-SOM with the land surface model SSiB5/TRIFFID to study the N limitation on GPP,NPP and LAI. It is interesting to implement the carbon-nitrogen study, which can be useful to earth system models or climate system models and it might improve the land-air interaction. The simulation with nitrogen shows the general better performance compared to that without N processes of SSiB4. But in some regions, such as Amazon and eastern China, the incorporation of N limitation leads to larger underestimation of carbon fluxes, which is needed to more discussion the mechanism in science.
Response:
Thank you for your constructive comments and suggestions, which help to improve the quality of our manuscript.
As you said, this paper mainly focuses on implementing the plant N processes, which are fundamental to plant physiology in the natural world but are not included in SSiB4, into the model SSiB5. After introducing N processes through our C-N framework, in general, SSiB5 has a lower bias for GPP than that of the baseline version of SSiB4. At the same time, the improvements are not homogeneous over all regions. We admit that the GPP simulation (underestimation) of SSiB5 in Amazon even gets a little bit worse.
In SSiB4’s simulation, there are some regions with lower GPP than observations. Therefore, the imposed N-limitation in SSiB5 would further increase the bias in the regions where the N-limitation is not dominant. In fact, when a new parameterization was introduced, it may not homogeneously improve the simulation everywhere because other deficient in the model. This mismatch is a common issue reported by a number of papers (Anav et al., 2013; Liu et al., 2019; Piao et al., 2013). Some studies (Gallup et al., 2021; Yan et al., 2017) speculated that the DGVMs poorly reproduce eddy covariance estimates, which affects Amazon rainforest gross primary productivity. In the revised manuscript, we have also added discussion in line 545-548 to address the shortcoming in Amazon and eastern China, and indicate this issue for the future investigation and made some suggestions based on current research.
References:
- Anav, A.; Murray-Tortarolo, G.; Friedlingstein, P.; Sitch, S.; Piao, S.; Zhu, Z. Evaluation of land surface models in reproducing satellite Derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models. Remote Sens. 2013, 5, 3637–3661
- Gallup, S. M., Baker, I. T., Gallup, J. L., Restrepo-Coupe, N., Haynes, K. D., Geyer, N. M., & Denning, A. S. (2021). Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask? Journal of Advances in Modeling Earth Systems, 13(8). https://doi.org/10.1029/ 2021MS002555
- Liu, Y., Xue, Y., Macdonald, G., Cox, P. and Zhang, Z.: Global vegetation variability and its response to elevated CO 2 , global warming, and climate variability - A study using the offline SSiB4/TRIFFID model and satellite data, Earth Syst. Dyn., 10(1), 9–29, doi:10.5194/esd-10-9-2019, 2019.
- Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X., Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Zaehle, S. and Zeng, N.: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends, Glob. Chang. Biol., 19(7), 2117–2132, doi:10.1111/gcb.12187, 2013.
- Yan, H., Wang, S. Q., Yu, K. L., Wang, B., Yu, Q., Bohrer, G., et al. (2017). A Novel Diffuse Fraction-Based Two-Leaf Light Use Efficiency Model: An Application Quantifying Photosynthetic Seasonality across 20 AmeriFlux Flux Tower Sites. Journal of Advances in Modeling Earth Systems, 9(6). https://doi.org/1002/2016MS000886
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AC1: 'Reply on RC1', Zheng Xiang, 19 Mar 2023
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RC2: 'Comment on egusphere-2022-1111', Anonymous Referee #2, 10 Jan 2023
General comments
Introducing a prognostic nutrient cycle, here the nitrogen cycle, into a land surface model (LSM) or a dynamic global vegetation model (DGVM) is a challenging task. As the importance of nutrient limitation on productivity has been clear for a while and we have gone from one LSM with a prognostic N cycle in CMIP5 to several in CMIP6 this is a step all LSM are and need to take. So, for undertaking this task and finishing an LSM that has included the N cycle I congratulate the authors. But, I’m very disappointed in the complexity and description of the N cycle that has been incorporated into SSiB/TRIFFID. Incorporating an N cycle myself into a model more than 10 years ago, I expect that current implementations should be of a higher standard. I have reviewed several N implementations over the years and expect a gradual improvement with the newest findings, especially from different MIPs like the Davies-Barnard et al. (2020) study that you have cited in your manuscript and newer model description papers (e.g. Wiltshire et al. 2021, Asaadi and Arora 2021). Also, there is no validation against any N-related properties or fluxes, only against GPP, LAI (difficult quantity to measure), SH, and LH. I also think that many of your statements in the introduction on the current state of N cycling in LSMs and DGVMs are wrong as are some of the interpretations of your results.
The focus of the experiments and the analysis of them is confusing to me. The highlight of this study is the implementation of dynamic C:N ratio (eqn 1), N limitation of Vmax (eqn 7-8), Ra (eqn 11), and phenology (eqn 14) and the results in figures 8f, 13a and 13b. These should be highlighted much more. Especially the results in figures 8f, 13a and 13b should be put together in one figure and explained in depth! Why you decide to introduce the strange N limitation directly on NPP and GPP and let it take such a large part of the study is puzzling to me. It makes the study confusing. I would exclude these and especially as you mention yourself that this isn’t the way N limitation works (lines 382-384).
The tone and description of existing models with N limitations are a shock to me. The feeling I get when reading the study is that all other models have various deficiencies (L56-57) and here is a model that has resolved this. I hope this wasn’t the message the authors were looking for, but many of the statement about other models in the study is wrong and misleading.
For the global comparison to GPP and LAI any limitation on GPP and LAI would improve the model. The important thing with introducing N limitation is to get it in the right locations. Otherwise, your model is missing something. Just reducing GPP and LAI as a global average can be done by just lowering your standard PFT Vmax value. Could probably get similar results just by optimising it.
In general, it would have been nice to see some perturbation experiments to see how SSiB4 vs SSiB5 would react to e.g. increased CO2 instead of the N limitation on NPP and GPP experiments.
I think this model description paper has many deficiencies. Several N-related processes aren’t documented and described properly. I would like to see a major revision and I hope my comments will be of some help.
Specific comments
L20-26 – Here you describe your “new” approach of N cycle modelling with dynamic C:N ratio in plant tissue, N limitation on productivity, growth (phenology), and autotrophic respiration. Models have had these features for at least 10 years now (Table A1 in Zaehle et al 2014 describes models participating in the FACE-MIP that have flexible C:N ratio and how N limitation affects growth: CABLE - Wang et al. (2011), DAYCENT - Parton et al. (2010); EALCO - Wang et al. (2001); GDAY - Comins & McMurtrie (1993); ISAM - Yang et al. (2009); LPJ-GUESS - Smith et al. (2014); OCN - Zaehle & Friend (2010); TECO - Weng & Lou (2008)). So, I don’t think you can call this a new approach.
L30-31 – Here you mention that you have more consistent respiration compared to observations. I can’t find any results related to respiration in the manuscript.
L39-47 – Here you state that “current” LSMs are oversimplistic and overestimate C sequestration under climate change. The references for these statements are very old and have been dealt with by the model developments (e.g. nutrient cycles). The Hungate et al (2003) estimates have been discussed in many studies (e.g. Smith et al 2014). I would say that a more current issue is that the models are getting too complex. So, I don’t agree with this section.
L50-54 – Here you refer to Davies-Barnard et al. (2020) study, but then you don’t mention the LSMs in that study when you list models with various representations of N processes e.g. Lawrence et al. 2019; Wiltshire et al. 2020; Smith et al. 2014.
L64-66 – I don’t agree with the statement “N limitation is represented as instantaneous downâregulation of potential photosynthesis rates based on soil mineral N availability” as most models use the current leaf status of nutrients to regulate potential photosynthesis rates and not the current mineral N availability in the soil. But I think this study does exactly that with eqn 8. If I’m not misunderstanding something, this statement criticises other models for something that they don’t do, but that this study then does.
L75-77 – Again, most models do have a flexible tissue C:N ratio. See Davies-Barnard et al. (2020) and Zaehle et al (2015) for a large selection of models with flexible C:N ratios.
L104-106, L146-148 – As SSiB/TRIFFID is a dynamic vegetation model it would be interesting to know how the N cycle has affected the PFT competition and hence the PFT fractional coverage.
L128 – Only two litter pools (metabolic and structural)? Is this sufficient for forest ecosystems with woody litter? DayCent have been mainly developed for agroecosystems and when used for the forested system then the addition of additional litter pools (fine and coarse woody litter pools) has been suggested by e.g. Kirschbaum et al (2002). How do you split woody litter into metabolic and structural litter and how are the litter pools compared to observation/estimates? The residence time of woody litter would be very short with only metabolic and structural litter pools.
Figure 1 – From the figure, it seems like SSiB4 doesn’t receive any information from any other part of the model, but the N cycle does affect GPP. Is the GPP from SSiB4 only affected by N limitation after it is calculated? If this is the case, how would this then affect the water demand for photosynthesis? Wouldn’t WUE be wrong if GPP is downregulated after the water demand is met? Too much water is used then as GPP is lowered afterwards.
L159 – Why validate against sensible and latent heat instead of some N variable?
L205-207 – Did you experience any runaway effects on soil C/N pools during the 2000 year offline spinup of the soil? And if so, how was it handled? How did the initial condition differ between the C-only and C:N versions of the model? Litter input and water usage (soil moisture), affecting decomposition, should be different depending on N limitation.
L209-213 – This whole section I have no idea what you are talking about. Most models only focus on the long-term effect when doing global studies? I would definitely argue the opposite. Just look at already cited papers and e.g. Peano et al. (2021), Boysen et al. (2021).
L217-218 – Why not compare the results to any N-related properties?
L241-243, Table 2 – It can be a little confusing what you mean by all four C-N coupling processes. First, you introduce 3 sets of experiments where f(N) limits Vmax (1, eqn 7), NPP (2, eqn 9a), and GPP (3, eqn 9b), then you say that the fourth experiment is including all four C-N coupling processes (Dyn C:N ratio, Vmax, Ra, and phenology). Normally you would expect all experiments to be connected so you can get the difference between them to get information on the feedback between them, but here you do some very strange things. I don’t understand why you do experiments 2 and 3 when you state that this is not the correct way of doing it (lines 382-384). It would be much more interesting if experiments 2 and 3 were looking at Ra (eqn 11) and LAI (eqn 12-14) N limitations separately.
L263-264 – I don’t agree with anything written in this section. The authors can’t really read any other modelling description papers on how the N cycle has been implemented to be able to make this statement.
L323, eqn 4 and 5 – So all soil mineral N is available for plant growth. No influence of the amount of roots a PFT has or anything else? Is this realistic?
L366-372, eqn 7 and 8 – Here the N limitation concept is introduced with eqn 8. f(N) is the fundament of the N cycle limitation. First, you mention that Vmax is related to leaf N concentration, but then you define f(N) as the ratio between today's growth and N availability for growth. The current leaf N concentration is nowhere to be seen. By doing it this way, which I don’t think makes any sense at all, you go against your own statement on line 82 where you state that your approach “prevent unrealistic instantaneous downâregulation of potential photosynthesis rates” but that is exactly what you end up doing when using only today's status as the determent of N limitation (f(N)).
L410 – How is ylm affected by temperature? A reference to where this is described is needed or the equation.
Equation 12-14 – p that affects LAI (eqn 12) is adjusted by f(N) (eqn 14). So, today's N limitation will dictate the full LAI. What happens if a single day has a very low f(N)? Is LAI also dropped to a very low value despite the N leaf concentration of already existing leaves could be very high? Does a lowering of LAI result in litter or does the LAI come back the next day if f(N) is higher? This instantaneous downâregulation is also against what is written on line 82.
Figure 5k – How can you have a higher GPP with SSiB5 compared to SSiB4 for the first years?
Figure 8 and 9 – In figure 8f we see the impact of the N cycle on GPP. When looking at Northern America N limitation seems to be strongest in crop and high-latitude grass areas. For Eurasia, crops and grassland again seem to be affected the strongest by N limitation. Surprisingly the boreal forest, which we assume has a very strong N limitation, doesn’t experience any effect on GPP when adding an N limitation. Why is this? Is the flexible C:N ratio too large that the N demand is always met? Instead, the tropical forests are N limited, which are assumed to not be N limited but P limited in reality. Why do you get these contrasting results compared to what one would like to get when introducing an N cycle to a model to represent N limitation on photosynthesis in areas we expect to be N limited (high latitudes and not around the equator)? Are you happy with these results?
L475-477 – GPP is too high in SSiB4 so any reduction to GPP would result in an improved seasonal cycle simulation. Is there a way that you could increase the N limitation to bring GPP even closer to the observations or lower the GPP in any other way?
L495 – LAI is more than twice the observations, so any decrease would reduce the bias. Is there any way to get the LAI closer to the observations? Now it is way off even with N limitation.
L546-547 – Can you make this statement from the results you have presented (NIPSN, NINPP, NIGPP)? And why do these experiments when you already in lines 382-384 state that they don’t make sense?
L547-549 – Any limitation on GPP and LAI would improve the model. The important thing with introducing N limitation is to get it in the right locations. Otherwise, your model is missing something. Just reducing GPP and LAI as a global average can be done by just lowering your standard PFT Vmax value. Could probably get similar results just by optimising it.
L574-577 – Here you express that you capture the global N limitation pattern as described in Du et al. (2020), but I would disagree. Why don’t you get any N limitation at all in the boreal forest regions? The “strong” latitudinal pattern of N limitation on GPP is due to grass N limitation and that the tropics are N limited for some reason. And what is meant by more comprehensive information? A better analysis than exists in Du et al. (2020)?
L577-579 – Why are only grass areas affected by N limitations on photosynthesis (Figure 13a) and only tropical areas affected by Ra and phenology N limitations (Figure 13b)? This needs to be explained! Also, how does N resorption vary between regions? This could be an indication of the N limitation of a system as well as mentioned in line 592.
L592-593 – Why is eqn 7 and 8, where the instantaneous downâregulation factor (f(N)) is used to downregulate Vmax, better than using well-established relationships between leaf nutrient concentration and Vmax (Walker et al. 2014, Ellsworth et al. 2022)? How does your approach have a unique advantage compared to these?
L601 – I haven’t seen any improvements to boreal forests when it comes to GPP.
L602 – Where is it shown that SSiB5 has a more realistic C:N ratio dynamics compared to SSiB4 (assuming that it is SSiB4 that you are comparing to here)? Haven’t seen any figure or result of this. I assume that SSiB4 has a fixed C:N ratio.
L606-607 – Don’t agree that SSiB5 can predict a global pattern of N limitation on GPP, when the strongest limitation is in the tropical region and boreal forest has none.
L615 – N limitation on Ra and phenology is effective in the tropics.
L? – There is no mention of BNF or N deposition in the manuscript. I’m assuming that BNF is done in DayCent and also N deposition is handled there. This needs to be mentioned as also which dataset is used as N deposition input.
Technical corrections
L92: remove “2-D”
L94: Remove “The results demonstrate the relative importance of different plant N processes in this C-N framework.” as it belongs to the Result section.
I’m not making any more technical comments as I think the manuscript needs major revision.
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Ellsworth, D.S., Crous, K.Y., De Kauwe, M.G. et al. Convergence in phosphorus constraints to photosynthesis in forests around the world. Nat Commun 13, 5005 (2022). https://doi.org/10.1038/s41467-022-32545-0
Goll, D. S., Brovkin, V., Parida, B. R., Reick, C. H., Kattge, J., Reich, P. B., van Bodegom, P. M., and Niinemets, Ü.: Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling, Biogeosciences, 9, 3547–3569, doi:10.5194/bg-9-3547-2012, 2012.
Hungate, B. A., Dukes, J. S., Shaw, M. R., Luo, Y. and Field, C. B.: Nitrogen and Climate Change, Science (80-. )., 302(5650), 1512–1513, doi:10.1126/science.1091390, 2003
Kirschbaum, M. U. F. and Paul, K. I.: Modelling C and N dynamics in forest soils with a modified version of the CENTURY model, Soil Biol. Biochem., 34, 341–354, 2002.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Kampenhout, L. van, Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Broeke, M. van den, Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V. and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
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Citation: https://doi.org/10.5194/egusphere-2022-1111-RC2 -
AC2: 'Reply on RC2', Zheng Xiang, 19 Mar 2023
The reviewer has raised many issues in his/her comments. All the questions and comments brought forward by the reviewer are carefully considered and responded to point by point. The revisions for the paper have been made accordingly in the resubmitted manuscript.
However, a number of comments/questions are repeated several times. We respond to those repeated/similar ones once, where we deemed them to be most appropriate, to make the answers more concise and easier to follow. In this way, we can provide a more comprehensive response for those questions with a clear overall picture rather than answer piece by piece spreading everywhere, which should help reviewer and editor easily check whether we properly address the comments.
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AC2: 'Reply on RC2', Zheng Xiang, 19 Mar 2023
Status: closed
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RC1: 'Comment on egusphere-2022-1111', Anonymous Referee #1, 04 Jan 2023
This work coupled the nitrogen model DayCent-SOM with the land surface moodel SSiB5/TRIFFID to study the N limitation on GPP,NPP and LAI. It is interesting to implement the carbon-nitrogen sutdy, which can be useful to earth system models or climate system models and it might improve the land-air interaction. The simulation with nitrogen shows the general better performance compared to that wihout N processes of SSiB4. But in some regions, such as Amazon and eastern China, the incorpoation of N limitation leads to larger underestimation of carbon fluxes, which is needed to more discussion the mechanism in science.
Citation: https://doi.org/10.5194/egusphere-2022-1111-RC1 -
AC1: 'Reply on RC1', Zheng Xiang, 19 Mar 2023
This work coupled the nitrogen model DayCent-SOM with the land surface model SSiB5/TRIFFID to study the N limitation on GPP,NPP and LAI. It is interesting to implement the carbon-nitrogen study, which can be useful to earth system models or climate system models and it might improve the land-air interaction. The simulation with nitrogen shows the general better performance compared to that without N processes of SSiB4. But in some regions, such as Amazon and eastern China, the incorporation of N limitation leads to larger underestimation of carbon fluxes, which is needed to more discussion the mechanism in science.
Response:
Thank you for your constructive comments and suggestions, which help to improve the quality of our manuscript.
As you said, this paper mainly focuses on implementing the plant N processes, which are fundamental to plant physiology in the natural world but are not included in SSiB4, into the model SSiB5. After introducing N processes through our C-N framework, in general, SSiB5 has a lower bias for GPP than that of the baseline version of SSiB4. At the same time, the improvements are not homogeneous over all regions. We admit that the GPP simulation (underestimation) of SSiB5 in Amazon even gets a little bit worse.
In SSiB4’s simulation, there are some regions with lower GPP than observations. Therefore, the imposed N-limitation in SSiB5 would further increase the bias in the regions where the N-limitation is not dominant. In fact, when a new parameterization was introduced, it may not homogeneously improve the simulation everywhere because other deficient in the model. This mismatch is a common issue reported by a number of papers (Anav et al., 2013; Liu et al., 2019; Piao et al., 2013). Some studies (Gallup et al., 2021; Yan et al., 2017) speculated that the DGVMs poorly reproduce eddy covariance estimates, which affects Amazon rainforest gross primary productivity. In the revised manuscript, we have also added discussion in line 545-548 to address the shortcoming in Amazon and eastern China, and indicate this issue for the future investigation and made some suggestions based on current research.
References:
- Anav, A.; Murray-Tortarolo, G.; Friedlingstein, P.; Sitch, S.; Piao, S.; Zhu, Z. Evaluation of land surface models in reproducing satellite Derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models. Remote Sens. 2013, 5, 3637–3661
- Gallup, S. M., Baker, I. T., Gallup, J. L., Restrepo-Coupe, N., Haynes, K. D., Geyer, N. M., & Denning, A. S. (2021). Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask? Journal of Advances in Modeling Earth Systems, 13(8). https://doi.org/10.1029/ 2021MS002555
- Liu, Y., Xue, Y., Macdonald, G., Cox, P. and Zhang, Z.: Global vegetation variability and its response to elevated CO 2 , global warming, and climate variability - A study using the offline SSiB4/TRIFFID model and satellite data, Earth Syst. Dyn., 10(1), 9–29, doi:10.5194/esd-10-9-2019, 2019.
- Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X., Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Zaehle, S. and Zeng, N.: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends, Glob. Chang. Biol., 19(7), 2117–2132, doi:10.1111/gcb.12187, 2013.
- Yan, H., Wang, S. Q., Yu, K. L., Wang, B., Yu, Q., Bohrer, G., et al. (2017). A Novel Diffuse Fraction-Based Two-Leaf Light Use Efficiency Model: An Application Quantifying Photosynthetic Seasonality across 20 AmeriFlux Flux Tower Sites. Journal of Advances in Modeling Earth Systems, 9(6). https://doi.org/1002/2016MS000886
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AC1: 'Reply on RC1', Zheng Xiang, 19 Mar 2023
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RC2: 'Comment on egusphere-2022-1111', Anonymous Referee #2, 10 Jan 2023
General comments
Introducing a prognostic nutrient cycle, here the nitrogen cycle, into a land surface model (LSM) or a dynamic global vegetation model (DGVM) is a challenging task. As the importance of nutrient limitation on productivity has been clear for a while and we have gone from one LSM with a prognostic N cycle in CMIP5 to several in CMIP6 this is a step all LSM are and need to take. So, for undertaking this task and finishing an LSM that has included the N cycle I congratulate the authors. But, I’m very disappointed in the complexity and description of the N cycle that has been incorporated into SSiB/TRIFFID. Incorporating an N cycle myself into a model more than 10 years ago, I expect that current implementations should be of a higher standard. I have reviewed several N implementations over the years and expect a gradual improvement with the newest findings, especially from different MIPs like the Davies-Barnard et al. (2020) study that you have cited in your manuscript and newer model description papers (e.g. Wiltshire et al. 2021, Asaadi and Arora 2021). Also, there is no validation against any N-related properties or fluxes, only against GPP, LAI (difficult quantity to measure), SH, and LH. I also think that many of your statements in the introduction on the current state of N cycling in LSMs and DGVMs are wrong as are some of the interpretations of your results.
The focus of the experiments and the analysis of them is confusing to me. The highlight of this study is the implementation of dynamic C:N ratio (eqn 1), N limitation of Vmax (eqn 7-8), Ra (eqn 11), and phenology (eqn 14) and the results in figures 8f, 13a and 13b. These should be highlighted much more. Especially the results in figures 8f, 13a and 13b should be put together in one figure and explained in depth! Why you decide to introduce the strange N limitation directly on NPP and GPP and let it take such a large part of the study is puzzling to me. It makes the study confusing. I would exclude these and especially as you mention yourself that this isn’t the way N limitation works (lines 382-384).
The tone and description of existing models with N limitations are a shock to me. The feeling I get when reading the study is that all other models have various deficiencies (L56-57) and here is a model that has resolved this. I hope this wasn’t the message the authors were looking for, but many of the statement about other models in the study is wrong and misleading.
For the global comparison to GPP and LAI any limitation on GPP and LAI would improve the model. The important thing with introducing N limitation is to get it in the right locations. Otherwise, your model is missing something. Just reducing GPP and LAI as a global average can be done by just lowering your standard PFT Vmax value. Could probably get similar results just by optimising it.
In general, it would have been nice to see some perturbation experiments to see how SSiB4 vs SSiB5 would react to e.g. increased CO2 instead of the N limitation on NPP and GPP experiments.
I think this model description paper has many deficiencies. Several N-related processes aren’t documented and described properly. I would like to see a major revision and I hope my comments will be of some help.
Specific comments
L20-26 – Here you describe your “new” approach of N cycle modelling with dynamic C:N ratio in plant tissue, N limitation on productivity, growth (phenology), and autotrophic respiration. Models have had these features for at least 10 years now (Table A1 in Zaehle et al 2014 describes models participating in the FACE-MIP that have flexible C:N ratio and how N limitation affects growth: CABLE - Wang et al. (2011), DAYCENT - Parton et al. (2010); EALCO - Wang et al. (2001); GDAY - Comins & McMurtrie (1993); ISAM - Yang et al. (2009); LPJ-GUESS - Smith et al. (2014); OCN - Zaehle & Friend (2010); TECO - Weng & Lou (2008)). So, I don’t think you can call this a new approach.
L30-31 – Here you mention that you have more consistent respiration compared to observations. I can’t find any results related to respiration in the manuscript.
L39-47 – Here you state that “current” LSMs are oversimplistic and overestimate C sequestration under climate change. The references for these statements are very old and have been dealt with by the model developments (e.g. nutrient cycles). The Hungate et al (2003) estimates have been discussed in many studies (e.g. Smith et al 2014). I would say that a more current issue is that the models are getting too complex. So, I don’t agree with this section.
L50-54 – Here you refer to Davies-Barnard et al. (2020) study, but then you don’t mention the LSMs in that study when you list models with various representations of N processes e.g. Lawrence et al. 2019; Wiltshire et al. 2020; Smith et al. 2014.
L64-66 – I don’t agree with the statement “N limitation is represented as instantaneous downâregulation of potential photosynthesis rates based on soil mineral N availability” as most models use the current leaf status of nutrients to regulate potential photosynthesis rates and not the current mineral N availability in the soil. But I think this study does exactly that with eqn 8. If I’m not misunderstanding something, this statement criticises other models for something that they don’t do, but that this study then does.
L75-77 – Again, most models do have a flexible tissue C:N ratio. See Davies-Barnard et al. (2020) and Zaehle et al (2015) for a large selection of models with flexible C:N ratios.
L104-106, L146-148 – As SSiB/TRIFFID is a dynamic vegetation model it would be interesting to know how the N cycle has affected the PFT competition and hence the PFT fractional coverage.
L128 – Only two litter pools (metabolic and structural)? Is this sufficient for forest ecosystems with woody litter? DayCent have been mainly developed for agroecosystems and when used for the forested system then the addition of additional litter pools (fine and coarse woody litter pools) has been suggested by e.g. Kirschbaum et al (2002). How do you split woody litter into metabolic and structural litter and how are the litter pools compared to observation/estimates? The residence time of woody litter would be very short with only metabolic and structural litter pools.
Figure 1 – From the figure, it seems like SSiB4 doesn’t receive any information from any other part of the model, but the N cycle does affect GPP. Is the GPP from SSiB4 only affected by N limitation after it is calculated? If this is the case, how would this then affect the water demand for photosynthesis? Wouldn’t WUE be wrong if GPP is downregulated after the water demand is met? Too much water is used then as GPP is lowered afterwards.
L159 – Why validate against sensible and latent heat instead of some N variable?
L205-207 – Did you experience any runaway effects on soil C/N pools during the 2000 year offline spinup of the soil? And if so, how was it handled? How did the initial condition differ between the C-only and C:N versions of the model? Litter input and water usage (soil moisture), affecting decomposition, should be different depending on N limitation.
L209-213 – This whole section I have no idea what you are talking about. Most models only focus on the long-term effect when doing global studies? I would definitely argue the opposite. Just look at already cited papers and e.g. Peano et al. (2021), Boysen et al. (2021).
L217-218 – Why not compare the results to any N-related properties?
L241-243, Table 2 – It can be a little confusing what you mean by all four C-N coupling processes. First, you introduce 3 sets of experiments where f(N) limits Vmax (1, eqn 7), NPP (2, eqn 9a), and GPP (3, eqn 9b), then you say that the fourth experiment is including all four C-N coupling processes (Dyn C:N ratio, Vmax, Ra, and phenology). Normally you would expect all experiments to be connected so you can get the difference between them to get information on the feedback between them, but here you do some very strange things. I don’t understand why you do experiments 2 and 3 when you state that this is not the correct way of doing it (lines 382-384). It would be much more interesting if experiments 2 and 3 were looking at Ra (eqn 11) and LAI (eqn 12-14) N limitations separately.
L263-264 – I don’t agree with anything written in this section. The authors can’t really read any other modelling description papers on how the N cycle has been implemented to be able to make this statement.
L323, eqn 4 and 5 – So all soil mineral N is available for plant growth. No influence of the amount of roots a PFT has or anything else? Is this realistic?
L366-372, eqn 7 and 8 – Here the N limitation concept is introduced with eqn 8. f(N) is the fundament of the N cycle limitation. First, you mention that Vmax is related to leaf N concentration, but then you define f(N) as the ratio between today's growth and N availability for growth. The current leaf N concentration is nowhere to be seen. By doing it this way, which I don’t think makes any sense at all, you go against your own statement on line 82 where you state that your approach “prevent unrealistic instantaneous downâregulation of potential photosynthesis rates” but that is exactly what you end up doing when using only today's status as the determent of N limitation (f(N)).
L410 – How is ylm affected by temperature? A reference to where this is described is needed or the equation.
Equation 12-14 – p that affects LAI (eqn 12) is adjusted by f(N) (eqn 14). So, today's N limitation will dictate the full LAI. What happens if a single day has a very low f(N)? Is LAI also dropped to a very low value despite the N leaf concentration of already existing leaves could be very high? Does a lowering of LAI result in litter or does the LAI come back the next day if f(N) is higher? This instantaneous downâregulation is also against what is written on line 82.
Figure 5k – How can you have a higher GPP with SSiB5 compared to SSiB4 for the first years?
Figure 8 and 9 – In figure 8f we see the impact of the N cycle on GPP. When looking at Northern America N limitation seems to be strongest in crop and high-latitude grass areas. For Eurasia, crops and grassland again seem to be affected the strongest by N limitation. Surprisingly the boreal forest, which we assume has a very strong N limitation, doesn’t experience any effect on GPP when adding an N limitation. Why is this? Is the flexible C:N ratio too large that the N demand is always met? Instead, the tropical forests are N limited, which are assumed to not be N limited but P limited in reality. Why do you get these contrasting results compared to what one would like to get when introducing an N cycle to a model to represent N limitation on photosynthesis in areas we expect to be N limited (high latitudes and not around the equator)? Are you happy with these results?
L475-477 – GPP is too high in SSiB4 so any reduction to GPP would result in an improved seasonal cycle simulation. Is there a way that you could increase the N limitation to bring GPP even closer to the observations or lower the GPP in any other way?
L495 – LAI is more than twice the observations, so any decrease would reduce the bias. Is there any way to get the LAI closer to the observations? Now it is way off even with N limitation.
L546-547 – Can you make this statement from the results you have presented (NIPSN, NINPP, NIGPP)? And why do these experiments when you already in lines 382-384 state that they don’t make sense?
L547-549 – Any limitation on GPP and LAI would improve the model. The important thing with introducing N limitation is to get it in the right locations. Otherwise, your model is missing something. Just reducing GPP and LAI as a global average can be done by just lowering your standard PFT Vmax value. Could probably get similar results just by optimising it.
L574-577 – Here you express that you capture the global N limitation pattern as described in Du et al. (2020), but I would disagree. Why don’t you get any N limitation at all in the boreal forest regions? The “strong” latitudinal pattern of N limitation on GPP is due to grass N limitation and that the tropics are N limited for some reason. And what is meant by more comprehensive information? A better analysis than exists in Du et al. (2020)?
L577-579 – Why are only grass areas affected by N limitations on photosynthesis (Figure 13a) and only tropical areas affected by Ra and phenology N limitations (Figure 13b)? This needs to be explained! Also, how does N resorption vary between regions? This could be an indication of the N limitation of a system as well as mentioned in line 592.
L592-593 – Why is eqn 7 and 8, where the instantaneous downâregulation factor (f(N)) is used to downregulate Vmax, better than using well-established relationships between leaf nutrient concentration and Vmax (Walker et al. 2014, Ellsworth et al. 2022)? How does your approach have a unique advantage compared to these?
L601 – I haven’t seen any improvements to boreal forests when it comes to GPP.
L602 – Where is it shown that SSiB5 has a more realistic C:N ratio dynamics compared to SSiB4 (assuming that it is SSiB4 that you are comparing to here)? Haven’t seen any figure or result of this. I assume that SSiB4 has a fixed C:N ratio.
L606-607 – Don’t agree that SSiB5 can predict a global pattern of N limitation on GPP, when the strongest limitation is in the tropical region and boreal forest has none.
L615 – N limitation on Ra and phenology is effective in the tropics.
L? – There is no mention of BNF or N deposition in the manuscript. I’m assuming that BNF is done in DayCent and also N deposition is handled there. This needs to be mentioned as also which dataset is used as N deposition input.
Technical corrections
L92: remove “2-D”
L94: Remove “The results demonstrate the relative importance of different plant N processes in this C-N framework.” as it belongs to the Result section.
I’m not making any more technical comments as I think the manuscript needs major revision.
References
Asaadi, A. and Arora, V. K.: Implementation of nitrogen cycle in the CLASSIC land model, Biogeosciences, 18, 669–706, https://doi.org/10.5194/bg-18-669-2021, 2021.
Boysen, L. R., Brovkin, V., Wårlind, D., Peano, D., Lansø, A. S., Delire, C., Burke, E., Poeplau, C., and Don, A.: Evaluation of soil carbon dynamics after forest cover change in CMIP6 land models using chronosequences, Environ. Res. Lett., 16, 074030, https://doi.org/10.1088/1748-9326/ac0be1, 2021.
Comins H, McMurtrie RE. 1993. Long-term biotic response of nutrient-limited forest ecosystems to CO2-enrichment: equilibrium behaviour of integrated plant–soil models. Ecological Applications 3: 666–681
Davies-Barnard, T., Meyerholt, J., Zaehle, S., Friedlingstein, P., Brovkin, V., Fan, Y., Fisher, R. A., Jones, C. D., Lee, H., Peano, D., Smith, B., Wårlind, D., and Wiltshire, A. J.: Nitrogen cycling in CMIP6 land surface models: progress and limitations, Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, 2020.
Ellsworth, D.S., Crous, K.Y., De Kauwe, M.G. et al. Convergence in phosphorus constraints to photosynthesis in forests around the world. Nat Commun 13, 5005 (2022). https://doi.org/10.1038/s41467-022-32545-0
Goll, D. S., Brovkin, V., Parida, B. R., Reick, C. H., Kattge, J., Reich, P. B., van Bodegom, P. M., and Niinemets, Ü.: Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling, Biogeosciences, 9, 3547–3569, doi:10.5194/bg-9-3547-2012, 2012.
Hungate, B. A., Dukes, J. S., Shaw, M. R., Luo, Y. and Field, C. B.: Nitrogen and Climate Change, Science (80-. )., 302(5650), 1512–1513, doi:10.1126/science.1091390, 2003
Kirschbaum, M. U. F. and Paul, K. I.: Modelling C and N dynamics in forest soils with a modified version of the CENTURY model, Soil Biol. Biochem., 34, 341–354, 2002.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Kampenhout, L. van, Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Broeke, M. van den, Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V. and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
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Citation: https://doi.org/10.5194/egusphere-2022-1111-RC2 -
AC2: 'Reply on RC2', Zheng Xiang, 19 Mar 2023
The reviewer has raised many issues in his/her comments. All the questions and comments brought forward by the reviewer are carefully considered and responded to point by point. The revisions for the paper have been made accordingly in the resubmitted manuscript.
However, a number of comments/questions are repeated several times. We respond to those repeated/similar ones once, where we deemed them to be most appropriate, to make the answers more concise and easier to follow. In this way, we can provide a more comprehensive response for those questions with a clear overall picture rather than answer piece by piece spreading everywhere, which should help reviewer and editor easily check whether we properly address the comments.
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AC2: 'Reply on RC2', Zheng Xiang, 19 Mar 2023
Data sets
SSiB5/TRIFFID/DayCent-SOM datasets for the paper's in-situ validations and global evaluations Yongkang Xue; Zheng Xiang https://doi.org/10.5281/zenodo.7196869
Model code and software
SSiB Version5/TRIFFID/DayCent-SOM Zheng Xiang https://doi.org/10.5281/zenodo.7297108
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