the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Jul 2021
The impacts of model structure, parameter uncertainty and experimental design on Earth system model simulations of litter bag decomposition experiments
Abstract. Accurate Earth system model simulations of the terrestrial carbon cycle and its feedbacks to climate critically depend on algorithms representing the decomposition of litter and soil organic matter. Litter bag studies, in which specific types of plant litter are subject to varying environmental conditions in the field and decomposition is measured, serve as valuable benchmarks for model performance. Here we test the Energy Exascale Earth System land model (ELM), which has two different structural representations of decomposition, using observations from the Long-term Intersite Decomposition Experiment (LIDET) over six different biomes and six different leaf litter types. We find that seasonal patterns in soil conditions and nutrient availability have large effects on decomposition rates, and that it is critical to include this in the simulation design. Despite widely differing base decomposition rates between the two different model structures, the models produce similar temporal patterns of decomposition when nitrogen is limiting. Both models overpredict the fraction of original nitrogen present as a function of carbon remaining when using default parameterizations. A parameter sensitivity analysis indicates strong dependence of model outputs on nitrogen limitation, carbon use efficiency and decomposition rates. A large spread in model predictions when considering an ensemble of possible parameter combinations strongly suggests parameter uncertainty may be more influential than model structural uncertainty, and that new measurement and modelling approaches may be necessary to constrain these uncertainties.
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RC1: 'Comment on bg-2021-163', Anonymous Referee #1, 10 Aug 2021
Ricciuto and co-authors replicate the study of Bonan et al. 2013 looking at litter decomposition rates that are applied in two different flavors of ELM using the CTC from CLM4 and the CNT (CENTURY-like scheme) applied in CLM4.5. Nuances here changes in nutrient availability over time and parametric uncertainty associated with the CTC model.
The authors seem keen to draw distinctions between the work presented here and that of Bonan et al., (e.g. lines 87-94, line 202, and the first several paragraphs of the discussion) in an apparent defense of the CTC model. I appreciate this motivation, but it seems somewhat petty and unproductive. More, it somewhat obscures the real distinctions that should be made between this study and the previous work. I’ve listed what seem like the real differences below.
Time evolving environmental scalars are used in both studies, and therefore NOT a real difference in the work presented. Bonan et al state in their methods “We calculated daily values of CDI by linearly interpolating between the specified monthly values to replace each model’s intrinsic calculation of temperature and moisture effects on decomposition rates”. It seems that the real difference here is that:
- Bonan et al. use the same environmental scalars that were derived from site meteorological history and applied to both models with
- Ricciuto et al. use the underlying state of ELM (using GSWP3 forcing) that are adjusted to site mean state (no details provided on how this was done) and use the surface soil temperature and moisture conditions to calculate environmental scalars for CTC and CNT.
- Both approaches presumably provide similar CDI values (or environmental scalars) as stated on line 481 (see also table 6 & Fig 3 & line 481). Thus, I don’t the boundary conditions of soil moisture and temperature are meaningfully differences in the experiment design.
Model selection also is significantly different, but this isn’t really discussed until line 500 of the test. There are several features in DAYCENT that are not applied in CLM4.5 / ELM-CNT, which (I think) include the:
- bi-directional transfer of C between SOM pools,
- rate modifiers on turnover and respiration fraction (CUE) that are a function of soil texture and pH
- Flexible stoichiometry of litter and SOM pools, which should make CENTURY less sensitive to external N availability. I would assume this is one of the bigger differences between this study and results in Bonan et al., but am only speculating.
- Maybe others…?
Bonan et al used the DAYCENT structure and parameterization, but my understanding of CNT is that the model is CENTURY-like (as is CTC by representing a first order decomposition cascade), but they aren’t really CENTURY or DAYCENT (e.g. Parton et al 1988, 1998). These comments should also be reflected in Fig 1, and the discussion (Line 472).
Indeed, looking at the RMSE reported in Table 3 of Bonan et al. and table 5 from this study one might conclude that CTC doesn’t look as bad when you lower the bar and compare it to a model also doesn’t do a very good job replicating LIDET observations (CNT). I don’t think this is the intended message the authors are trying to convey.
Treatment of potential N limitation of litter decomposition is different between the Bonan study and this work, with this study using FPI passed as a boundary condition from ELM simulations. The argument in line 510 seems to be that rapid carbon turnover in CTC creates high N demand from decaying litter which is subsequently downregulated by N availability. This seems qualitatively similar to conclusions by Bonan et al. who state “CLM-cn requires severe reduction in N-optimal decomposition rates to match the observations.” Indeed, the conclusion by Bonan et al seem consistent with results reported here showing that CTC requires strong N limitation of decay rates (Fig 3), to achieve decay rates that are still faster than LIDET observations (Fig 2), and result in a higher than observed immobilization of N (Fig 4). Am I missing something, or do these results largely confirm findings by Bonan et al regarding assumptions about N limitation that seem built into the implementation of the CTC model?
Moreover, I’m not sure this assumption of strong N limitation of litter decay is well supported in the literature, a point which is notably absent from the discussion (e.g. Knorr et al. 2005, Hobbie 2008, as well as discussion in Bonan et al. 2013). Notably, the only reference cited here (Cleveland & Townsend 2006) is for soil respiration. Another paper by the same authors (Cleveland et al. 2006) found no difference in rates of leaf litter decomposition with N or P fertilization, a finding that seems more relevant to the results present here.
The parameter sensitivity test (Figs 7-9) show that parameter adjustments can make soil CTC results look more similar to the CNT simulations. This is not surprising since the models share a nearly identical structure and theoretical basis. Here the use of the soil biogeochemistry unit test outside of ELM limit the utility of this sensitivity test. Presumably parameter combinations that reduce decomposition rates closer to the observations will have cascading effects on N mineralization rates, plant productivity, and total soil C storage. I appreciate that the litter decomposition model used here may not resolve all these feedbacks, but I would be interesting to discuss how improvements to the parameters used in these soil models may feedback to the terrestrial ecosystems ELM is intended to simulate.
Finally, I would consider breaking up lengthy paragraphs in the discussion. I also found the discussion rather long and wandering, with too much of a critique of previous work and too little interpretation of the results presented in this study. Subheadings may also help provide some structure for readers and guide the development of ideas. Finally, I find it helpful when display items are referred to directly in the discussion. This is done very sparingly now.
Minor and technical points
Left and right panels should be combined in Fig 2. The point of the paper is to compare decomposition rates that are simulated by CTC and CNT decomposition cascades. Separating the results onto different panels obscures this comparison.
Line 285, why average over litter types? The LIDET study showed that within biomes litter quality strongly influences decomposition rates. This effect was communicated in Bonan et al 2013 by showing the standard deviation in percent mass remaining for each model for each biome. Their results show that the CTC approach was much less sensitive to initial litter quality than DAYCENT. I’m assuming that same is true here, but this information should also be shown on display items.
Line 310, temperature and moisture scalars do not seem to be identical for the models (Fig 3, right panels). It seems additional information about each formulation should be provided in the methods. Maybe the shape of each function can be included in the appendix.
Line 317- this seems to be the chief difference between the CTC and CNT approaches. CTC has higher base decomposition rates that are downregulated by nitrogen limitation, resulting in what the authors generally deem acceptable agreement with LIDET observations. This presents an interesting set of assumptions in the two model parameterizations that should be testable with experimental results. I know LIDET didn’t manipulate N availability, but the assumption in CTC that decomposition rates would be much larger with increased inorganic N availability does not seem well supported by N fertilization experiments that look at decomposition rates. What does this mean for assumptions and the parameterization of the CTC model?
The introduction motivates this study by the need to improve representation of soil C turnover times and stocks in Earth system models. Given this motivation would adding total soil C stocks to Table 6 be instructive? Alternatively, a plot showing NPP-SOC relationships across the LIDET sites for both models may be helpful for illustrating similarities / differences among the models?
Fig 4 should also include TRAE results, I would guess they show massive N immobilization, but should be included for completeness.
Line 175, are the underlying litter and soil C pools spun up before the litter begins? If so, how? If not, why?
What happens to L3 in the CTC litterbag. Does it decompose in this experiment, and if so, where do C fluxes go?
Line 202, this sentence is misleading since both this study and Bonan et al 2013 use a “functional unit representation”. Moreover, both studies use time evolving environmental scalars (see comment above).
Line 360, why are litter decomposition rates sensitive to the turnover times of SOM pools in CTC? After roughly year 1 of the experiment the sensitivity of litter decay is more strongly controlled by SOM turnover than turnover of litter pools (and allocation to metabolic vs. ligni litter pools is almost never important?!) Is this because you’re counting everything in the ‘litterbag layer’ including LIT and SOM pools? If so, it suggests that a bunch of material is moving into SOM in one year. This may be expected at the tropical BCI site, but it seems unrealistic at a much colder site like BNZ.
Similarly, litter decay rates even show sensitivity to the turnover of SOM4 at BCI by the end of the decade long LIDET experiment in Fig 5. I know these pools are somewhat arbitrary, but even tropical forests have SOM with old radiocarbon ages. I would assume that SOM4 is supposed to be broadly representative of this ‘passive’ soil C pool, but sensitivity of litter decay rates to the parameterization of SOM4 suggests that C, and by extension N, is really blazing through CTC in ways that may not be consistent with our understanding of factors controlling persistence of soil C stocks. This point seems warranted in the discussion of results. See also line 414, where the C:N ratio of SOM4 is important for N dynamics in the litter bag study.
Line 473, Bonan et al use DAYCENT, which is not the same as the CNT model being tested here. I’d suspect these differences reported by Bonan et al have more to do with the model structure than the experimental design.
Line 482, you’d have to check with the authors on the Bonan paper, but as mentioned above I don’t think that seasonal variation is neglected in their work.
Line 487, I appreciate that ELM assumes strong N limitation of litter decomposition, but I would appreciate references supporting this assumption from experimental manipulations of real ecosystems.
Line 491, this is true, Bonan’s work is not vertically resolved, but it’s also using DAYCENT, not the CENTURY-like approach applied by Koven et al.
Line 514, I either don’t understand or strongly disagree with this statement. This work seems to suggest that turnover rates are strongly linked to net N demand (and mineralization and immobilization). Indeed, the only two things soil biogeochemistry does in a model like ELM are store (or lose) carbon which is important for climate-carbon feedbacks, and mineralization nitrogen (controlling the magnitude of plant response to warming and elevated CO2). Both of these seem directly linked the parameterization of the soil biogeochemistry model.
Line 534, reference needed to support this claim.
Lines 535-555 this shout out to some microbial explicit models is nice, but I’m not sure how it relates to the work presented here (especially related to dynamic CUE).
Line 550, If this text stays in the revised paper see also Wieder et al. 2015, Kyker-Snowman et al. 2020.
Line 555-575. This catchall paragraph that talks about N immobilization, home field advantage, and UV degradation of litter kind of makes my head spin. Maybe break this up and bring some of the ideas back to how they may be considered in a model like ELM (if at all). Notably, it seems the N immobilization dynamic (Fig 4) seem critical to address for the use of these soil models in ELM?
Line 558, I don’t understand why you need to invoke N losses from litterbags here. Couldn’t rates of immobilization simply be too high (e.g. modeled litterbags translocate too much N from surface soils or surface soils have unrealistically high C (and therefore N turnover) that’s available for litter bag immobilization?
Line 576, I agree with this statement, but don’t see how the work presented helps us think about soil organic matter, as it’s a litter decomposition study.
References:
Cleveland, C.C., Reed, S.C. and Townsend, A.R. (2006), NUTRIENT REGULATION OF ORGANIC MATTER DECOMPOSITION IN A TROPICAL RAIN FOREST. Ecology, 87: 492-503. https://doi.org/10.1890/05-0525
Hobbie SE (2008) Nitrogen effects on decomposition: a five-year experiment in eight temperate sites. Ecology, 89, 2633–2644.
Knorr, M., Frey, S.D. and Curtis, P.S. (2005), NITROGEN ADDITIONS AND LITTER DECOMPOSITION: A META-ANALYSIS. Ecology, 86: 3252-3257. https://doi.org/10.1890/05-0150
Kyker-Snowman, E., Wieder, W. R., Frey, S. D., & Grandy, A. S. (2020). Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0). Geoscientific Model Development, 13(9), 4413-4434. doi: 10.5194/gmd-13-4413-2020.
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., Taylor, P. G., & Bonan, G. B. (2015). Representing life in the Earth system with soil microbial functional traits in the MIMICS model. Geoscientific Model Development, 8(6), 1789-1808. doi: 10.5194/gmd-8-1789-2015.
Citation: https://doi.org/10.5194/bg-2021-163-RC1 -
AC1: 'Reply on RC1', Daniel Ricciuto, 15 Mar 2022
We thank the reviewer for the detailed comments and feedback. We copy the review below with responses in bold text for each specific point.
Ricciuto and co-authors replicate the study of Bonan et al. 2013 looking at litter decomposition rates that are applied in two different flavors of ELM using the CTC from CLM4 and the CNT (CENTURY-like scheme) applied in CLM4.5. Nuances here changes in nutrient availability over time and parametric uncertainty associated with the CTC model.
The authors seem keen to draw distinctions between the work presented here and that of Bonan et al., (e.g. lines 87-94, line 202, and the first several paragraphs of the discussion) in an apparent defense of the CTC model. I appreciate this motivation, but it seems somewhat petty and unproductive. More, it somewhat obscures the real distinctions that should be made between this study and the previous work. I’ve listed what seem like the real differences below.
We greatly appreciate the perspective from an objective viewpoint and will adjust the text and tone accordingly following the more detailed suggestions below.
Time evolving environmental scalars are used in both studies, and therefore NOT a real difference in the work presented. Bonan et al state in their methods “We calculated daily values of CDI by linearly interpolating between the specified monthly values to replace each model’s intrinsic calculation of temperature and moisture effects on decomposition rates”. It seems that the real difference here is that:
- Bonan et al. use the same environmental scalars that were derived from site meteorological history and applied to both models with
- Ricciuto et al. use the underlying state of ELM (using GSWP3 forcing) that are adjusted to site mean state (no details provided on how this was done) and use the surface soil temperature and moisture conditions to calculate environmental scalars for CTC and CNT.
- Both approaches presumably provide similar CDI values (or environmental scalars) as stated on line 481 (see also table 6 & Fig 3 & line 481). Thus, I don’t the boundary conditions of soil moisture and temperature are meaningfully differences in the experiment design.
It is correct that the CDI used by Bonan et al. represents the seasonal pattern of environmental scalars. We emphasize here that we use the environmental scalars that are consistent with those that are used in simulations of ELM, the full land-surface model. We will modify the text accordingly and provide additional detail about how the environmental scalars are calculated in the updated manuscript.
Model selection also is significantly different, but this isn’t really discussed until line 500 of the test. There are several features in DAYCENT that are not applied in CLM4.5 / ELM-CNT, which (I think) include the:
- bi-directional transfer of C between SOM pools,
- rate modifiers on turnover and respiration fraction (CUE) that are a function of soil texture and pH
- Flexible stoichiometry of litter and SOM pools, which should make CENTURY less sensitive to external N availability. I would assume this is one of the bigger differences between this study and results in Bonan et al., but am only speculating.
- Maybe others…?
Bonan et al used the DAYCENT structure and parameterization, but my understanding of CNT is that the model is CENTURY-like (as is CTC by representing a first order decomposition cascade), but they aren’t really CENTURY or DAYCENT (e.g. Parton et al 1988, 1998). These comments should also be reflected in Fig 1, and the discussion (Line 472).
We agree that the implementation of ELM-CNT is significantly different than DAYCENT, and therefore we cannot imply that the results from DAYCENT are applicable to ELM-CNT or vice versa. We will add a paragraph describing the differences among these models to the methods section and revise the text in the discussion to emphasize these points.
Indeed, looking at the RMSE reported in Table 3 of Bonan et al. and table 5 from this study one might conclude that CTC doesn’t look as bad when you lower the bar and compare it to a model also doesn’t do a very good job replicating LIDET observations (CNT). I don’t think this is the intended message the authors are trying to convey.
One of the messages that we attempt to convey here is that indeed the CNT and CTC implementations are quite similar in performance. This may help to avoid confusion about the relevance of analyses based on DAYCENT for the implementation of CNT in CLM/ELM. It can certainly be argued that DAYCENT has superior results to both implementations in ELM, but these ELM approaches have been used for a number of global offline and coupled experiments. Therefore, we believe the knowledge that both approaches are similar in performance is useful for the community. Despite similar model structures, key parameters (e.g. turnover times) in the two models are actually quite different. Therefore, the similar performance was actually not something we expected to see until we performed further analysis of the nutrient feedbacks.
Treatment of potential N limitation of litter decomposition is different between the Bonan study and this work, with this study using FPI passed as a boundary condition from ELM simulations. The argument in line 510 seems to be that rapid carbon turnover in CTC creates high N demand from decaying litter which is subsequently downregulated by N availability. This seems qualitatively similar to conclusions by Bonan et al. who state “CLM-cn requires severe reduction in N-optimal decomposition rates to match the observations.” Indeed, the conclusion by Bonan et al seem consistent with results reported here showing that CTC requires strong N limitation of decay rates (Fig 3), to achieve decay rates that are still faster than LIDET observations (Fig 2), and result in a higher than observed immobilization of N (Fig 4). Am I missing something, or do these results largely confirm findings by Bonan et al regarding assumptions about N limitation that seem built into the implementation of the CTC model?
Our results do show that N limitation is an important factor regulating litter decomposition in the ELM simulations. Since this control by N availability is the intended behavior of the ELM model, the main point is that simulated decomposition rates match LIDET observations more closely than implied by Bonan et al. 2013 (their Figure 3). We will adjust the text to better motivate and describe the comparison with Bonan et al.
Moreover, I’m not sure this assumption of strong N limitation of litter decay is well supported in the literature, a point which is notably absent from the discussion (e.g. Knorr et al. 2005, Hobbie 2008, as well as discussion in Bonan et al. 2013). Notably, the only reference cited here (Cleveland & Townsend 2006) is for soil respiration. Another paper by the same authors (Cleveland et al. 2006) found no difference in rates of leaf litter decomposition with N or P fertilization, a finding that seems more relevant to the results present here.
We agree that the discussion should better address this point and we will add a paragraph to the text. One argument for the N inhibition of decomposition is the strong dependence of mass loss on litter quality observed in LIDET and other litter bag experiments, including litter N or C/N ratio independent of lignin content (Cornwell et al., 2008; Zhang et al., 2008). We will also add a figure showing the variation of mass loss with litter type and how this compares with the observed values. Fertilization studies show more mixed results. While some studies show that N addition does not strongly limit litter decay, others do indicate limitation. Knorr et al. (2005) reports inhibition for low-quality (i.e. high lignin) litters and for long decay periods. Stimulation was observed for shorter periods and for high quality litter. A recent study by Gill et al. (2021) also indicates that N fertilization accelerates decomposition of leaf litter initially, followed by slower decomposition later.
Cornwell, W. K., Cornelissen, J. H., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., et al. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071. doi: 10.1111/j.1461-0248.2008.01219.x
Gill, A., J. Schilling, S.E. Hobbie (2021) Experimental nitrogen fertilization globally accelerates, then slows decomposition of leaf litter. Ecology Letters, 24(4), 802-811, https://doi.org/10.1111/ele.13700
Knorr, M., S.D. Frey, P.S. Curtis (2005) Nitrogen additions and litter decomposition: A meta-analysis. Ecology, 86(12), 3252-3257, https://doi.org/10.1890/05-0150
Deqiang Zhang, Dafeng Hui, Yiqi Luo, Guoyi Zhou, Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors, Journal of Plant Ecology, Volume 1, Issue 2, June 2008, Pages 85–93, https://doi.org/10.1093/jpe/rtn002
The parameter sensitivity test (Figs 7-9) show that parameter adjustments can make soil CTC results look more similar to the CNT simulations. This is not surprising since the models share a nearly identical structure and theoretical basis. Here the use of the soil biogeochemistry unit test outside of ELM limit the utility of this sensitivity test. Presumably parameter combinations that reduce decomposition rates closer to the observations will have cascading effects on N mineralization rates, plant productivity, and total soil C storage. I appreciate that the litter decomposition model used here may not resolve all these feedbacks, but I would be interesting to discuss how improvements to the parameters used in these soil models may feedback to the terrestrial ecosystems ELM is intended to simulate.
Thank you for bringing up this point. A parameter sensitivity analysis for the full ELM model is useful to better understand the impact of feedbacks between litter decomposition, nutrient cycling, and vegetation feedbacks. Previous studies (e.g. Ricciuto et al., 2018) showed that these model parameters were not especially sensitive. We will update the manuscript with an additional figure showing how the sensitive parameters in the decomposition model impact the full ELM.
Finally, I would consider breaking up lengthy paragraphs in the discussion. I also found the discussion rather long and wandering, with too much of a critique of previous work and too little interpretation of the results presented in this study. Subheadings may also help provide some structure for readers and guide the development of ideas. Finally, I find it helpful when display items are referred to directly in the discussion. This is done very sparingly now.
Thank you for the suggestion. We will revise the discussion with more focus on interpreting results in the study, and break it up with subheadings.
Minor and technical points
Left and right panels should be combined in Fig 2. The point of the paper is to compare decomposition rates that are simulated by CTC and CNT decomposition cascades. Separating the results onto different panels obscures this comparison.
We will revise this figure as suggested.
Line 285, why average over litter types? The LIDET study showed that within biomes litter quality strongly influences decomposition rates. This effect was communicated in Bonan et al 2013 by showing the standard deviation in percent mass remaining for each model for each biome. Their results show that the CTC approach was much less sensitive to initial litter quality than DAYCENT. I’m assuming that same is true here, but this information should also be shown on display items.
We will revise the figure to show the variation over litter types similar to Bonan et al.’s figure, and include a supplementary figure that shows the responses for individual litter types.
Line 310, temperature and moisture scalars do not seem to be identical for the models (Fig 3, right panels). It seems additional information about each formulation should be provided in the methods. Maybe the shape of each function can be included in the appendix.
There are some differences between the models, we will include the equations for the temperature and moisture scalars in the supplementary material.
Line 317- this seems to be the chief difference between the CTC and CNT approaches. CTC has higher base decomposition rates that are downregulated by nitrogen limitation, resulting in what the authors generally deem acceptable agreement with LIDET observations. This presents an interesting set of assumptions in the two model parameterizations that should be testable with experimental results. I know LIDET didn’t manipulate N availability, but the assumption in CTC that decomposition rates would be much larger with increased inorganic N availability does not seem well supported by N fertilization experiments that look at decomposition rates. What does this mean for assumptions and the parameterization of the CTC model?
Decomposition shows mixed responses to fertilization experiments, but shows a strong dependence on litter C:N ratios (see above response). Without strong N limitation, we would only see a litter quality effect in the simulation through lignin content, but not C:N ratio.
The introduction motivates this study by the need to improve representation of soil C turnover times and stocks in Earth system models. Given this motivation would adding total soil C stocks to Table 6 be instructive? Alternatively, a plot showing NPP-SOC relationships across the LIDET sites for both models may be helpful for illustrating similarities / differences among the models?
Thank you for this great suggestion. We will revise the table to add C stocks.
Fig 4 should also include TRAE results, I would guess they show massive N immobilization, but should be included for completeness.
We will revise the figure to include TRAE results.
Line 175, are the underlying litter and soil C pools spun up before the litter begins? If so, how? If not, why?
Yes, the litter and SOM pools are spun up using a 200-year accelerated decomposition framework commonly used in ELM (Thornton and Rosenbloom, 2005), followed by a “regular” spinup for 600 additional years.
What happens to L3 in the CTC litterbag. Does it decompose in this experiment, and if so, where do C fluxes go?
L3 decomposes into SOM3. There is a missing arrow in the figure, we apologize for this error and will correct it in the revised manuscript.
Line 202, this sentence is misleading since both this study and Bonan et al 2013 use a “functional unit representation”. Moreover, both studies use time evolving environmental scalars (see comment above).
We will revise this sentence to avoid misleading the reader. Because both use functional units and time evolving environmental scalars, the main point is that our study uses time-varying nitrogen availability from the full ELM.
Line 360, why are litter decomposition rates sensitive to the turnover times of SOM pools in CTC? After roughly year 1 of the experiment the sensitivity of litter decay is more strongly controlled by SOM turnover than turnover of litter pools (and allocation to metabolic vs. ligni litter pools is almost never important?!) Is this because you’re counting everything in the ‘litterbag layer’ including LIT and SOM pools? If so, it suggests that a bunch of material is moving into SOM in one year. This may be expected at the tropical BCI site, but it seems unrealistic at a much colder site like BNZ.
This is correct, in our model the “litterbag” includes both litter and SOM pools. Given the turnover times in CTC, a significant fraction of the litter is turning over to SOM. We suspect that the base turnover rates for litter pools are relatively insensitive not because everything has transitioned to SOM, but because actual rates are a function of parameters that control N availability. In the revised MS, we will conduct additional sensitivity analyses with fixed FPI to better understand the impacts of N limitation.
Similarly, litter decay rates even show sensitivity to the turnover of SOM4 at BCI by the end of the decade long LIDET experiment in Fig 5. I know these pools are somewhat arbitrary, but even tropical forests have SOM with old radiocarbon ages. I would assume that SOM4 is supposed to be broadly representative of this ‘passive’ soil C pool, but sensitivity of litter decay rates to the parameterization of SOM4 suggests that C, and by extension N, is really blazing through CTC in ways that may not be consistent with our understanding of factors controlling persistence of soil C stocks. This point seems warranted in the discussion of results. See also line 414, where the C:N ratio of SOM4 is important for N dynamics in the litter bag study.
Given the base turnover time of ~30 years for SOM4, we do begin to see some sensitivity related to parameters controlling the decomposition of this pool at the end of 10 years. We agree that this is an important point and will add some discussion. Old radiocarbon ages generally occur at greater soil depths while our LIDET analysis only focuses on the top soil layer. For both CNT and CTC, ELM includes a depth-dependent decomposition factor which may greatly decrease decomposition rates (Koven et al., 2013) at depth. Simulated radiocarbon profiles are highly sensitive to the parameter controlling this depth dependence. We believe addressing this is beyond the scope of this study, but will look more closely at this in future work as a more comprehensive evaluation of CTC.
Line 473, Bonan et al use DAYCENT, which is not the same as the CNT model being tested here. I’d suspect these differences reported by Bonan et al have more to do with the model structure than the experimental design.
Thank you for the comment, we will revise this sentence accordingly.
Line 482, you’d have to check with the authors on the Bonan paper, but as mentioned above I don’t think that seasonal variation is neglected in their work.
We will revise this to indicate the key difference is for dynamics/seasonal dependence of N limitation, not environmental scalars.
Line 487, I appreciate that ELM assumes strong N limitation of litter decomposition, but I would appreciate references supporting this assumption from experimental manipulations of real ecosystems.
We agree that this is a very important topic for discussion. Please see the response above.
Line 491, this is true, Bonan’s work is not vertically resolved, but it’s also using DAYCENT, not the CENTURY-like approach applied by Koven et al.
Here we intended to discuss the difference between and relevance of Bonan’s implementation of CTC and ours. We will revise the text for clarity.
Line 514, I either don’t understand or strongly disagree with this statement. This work seems to suggest that turnover rates are strongly linked to net N demand (and mineralization and immobilization). Indeed, the only two things soil biogeochemistry does in a model like ELM are store (or lose) carbon which is important for climate-carbon feedbacks, and mineralization nitrogen (controlling the magnitude of plant response to warming and elevated CO2). Both of these seem directly linked the parameterization of the soil biogeochemistry model.
This sentence was worded poorly. The model is relatively insensitive to the base rates of litter decomposition, but by definition must be sensitive to the soil biogeochemistry model parameters. Our sensitivity analysis indicates that the sensitivity of actual decomposition rates over the course of the 10-year period is related to parameters driving rates of immobilization and mineralization, mainly the respiration factors (rf parameters) and SOM decomposition base rates. We will revise this sentence for clarity and expand the sensitivity analysis as suggested above to better make these points.
Line 534, reference needed to support this claim.
We will add a reference here.
Lines 535-555 this shout out to some microbial explicit models is nice, but I’m not sure how it relates to the work presented here (especially related to dynamic CUE).
This work suggests the high sensitivity of decomposition to CUE, indicating that considering dynamic CUE may strongly impact Earth system model simulations. We agree this section is too long considering we are not addressing this topic directly, and will shorten it.
Line 550, If this text stays in the revised paper see also Wieder et al. 2015, Kyker-Snowman et al. 2020.
Thank you for the suggested references, we will add them as appropriate considering the previous comment.
Line 555-575. This catchall paragraph that talks about N immobilization, home field advantage, and UV degradation of litter kind of makes my head spin. Maybe break this up and bring some of the ideas back to how they may be considered in a model like ELM (if at all). Notably, it seems the N immobilization dynamic (Fig 4) seem critical to address for the use of these soil models in ELM?
We will break up this discussion into separate topics and focus more discussion on the topic of N immobilization. In particular, Fig. 7 indicates a large range of trajectories for the fraction of N remaining, reflecting different N immobilization dynamics for different combinations of parameters. We will conduct some additional analysis of our ensemble simulations to determine whether it is possible through parameterization to reproduce both the observed C loss and the trajectory of N. This will provide more information for analysis and discussion of the appropriateness of our model structure.
Line 558, I don’t understand why you need to invoke N losses from litterbags here. Couldn’t rates of immobilization simply be too high (e.g. modeled litterbags translocate too much N from surface soils or surface soils have unrealistically high C (and therefore N turnover) that’s available for litter bag immobilization?
We will remove possible N losses from the discussion as it is not relevant to the results. The factors mentioned all may be important in addition to possible loss of C from the litterbag from leaching. We will revise the discussion to add the points.
Line 576, I agree with this statement, but don’t see how the work presented helps us think about soil organic matter, as it’s a litter decomposition study.
We will remove this sentence from the conclusion.
Citation: https://doi.org/10.5194/bg-2021-163-AC1
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RC2: 'Comment on bg-2021-163', Anonymous Referee #2, 02 Sep 2021
This paper is a modelling study looking at whether two existing soil process representations within the ELM model can capture observations of litter decomposition from a distributed litterbag experiment. The authors devise a modelling framework - a functional unit approach - to easily reproduce the large number of observations as well as our conceptual understanding of decomposition inside litterbags. The authors conclude that the two different representations have a similar capability to reproduce the observations.
The modelling approach is robust and the manuscript is well written, but I find the authors downplay their results in the discussion section. This is largely a comparison with another similar study and how their modelling approach and results differ from that of Bonan et al., 2013. I am not very familiar with that study and cannot provide a point by point comparison, but surely there is more to the paper under review than whether it does or does not produce the same results as a paper from 8 years ago? Specifically, I think it would be interesting to discuss more the implications of the results and future modelling directions. How can we improve our models? What other data should we use? Should we focus more on parameterisation than structure, as the parameter sensitivity suggests? These points are touched briefly in the last couple of paragraphs but could be emphasized more for a stronger paper.
A couple of other points that would make the paper easier to read: there are a lot of acronyms, especially related to the site names and litter types; I wonder if these could be replaced with more intuitive names, especially the litter types? E.g. high/low lignin or high/low CN? Also, the introduction could be improved by some headings to guide the reader.
Minor comments
Table 1 - It would help to add lat/lon information as not everyone is familiar with the exact location of US states.
L 146 Are the temperature, moisture etc modifiers common to the two models?L 150 What is the respiration a fraction of?
L 178 How long was the spinup period and how was steady state determined?
L 186 How was this adjustment done?
L 546 ‘model’ repeated
L 558 Missing bracket
Citation: https://doi.org/10.5194/bg-2021-163-RC2 -
AC2: 'Reply on RC2', Daniel Ricciuto, 15 Mar 2022
We thank the reviewer for the thoughtful comments. The review is copied below with resposnes in bold text.
This paper is a modelling study looking at whether two existing soil process representations within the ELM model can capture observations of litter decomposition from a distributed litterbag experiment. The authors devise a modelling framework - a functional unit approach - to easily reproduce the large number of observations as well as our conceptual understanding of decomposition inside litterbags. The authors conclude that the two different representations have a similar capability to reproduce the observations.
The modelling approach is robust and the manuscript is well written, but I find the authors downplay their results in the discussion section. This is largely a comparison with another similar study and how their modelling approach and results differ from that of Bonan et al., 2013. I am not very familiar with that study and cannot provide a point by point comparison, but surely there is more to the paper under review than whether it does or does not produce the same results as a paper from 8 years ago? Specifically, I think it would be interesting to discuss more the implications of the results and future modelling directions. How can we improve our models? What other data should we use? Should we focus more on parameterisation than structure, as the parameter sensitivity suggests? These points are touched briefly in the last couple of paragraphs but could be emphasized more for a stronger paper.
Thank you for the comments. We agree that the manuscript focused too much on the comparison with Bonan et al. and not enough on the implications of the new results presented here. We focus here on how the nutrient dynamics influence the turnover rates, and will re-organize the discussion to highlight these results and put them in the context of 1) previous studies and 2) how additional data and modeling may help to resolve model parameter and structural uncertainties. The discussion will be reorganized to emphasize these points.
A couple of other points that would make the paper easier to read: there are a lot of acronyms, especially related to the site names and litter types; I wonder if these could be replaced with more intuitive names, especially the litter types? E.g. high/low lignin or high/low CN? Also, the introduction could be improved by some headings to guide the reader.
Thank you for the suggestions. We would like to keep the litter names for consistency with previous studies, but will indicate the C/N ratios next to the litter name. We will edit the text to use more descriptive site names.
Minor comments
Table 1 - It would help to add lat/lon information as not everyone is familiar with the exact location of US states.
We will add the lat/lon information to the site table.
L 146 Are the temperature, moisture etc modifiers common to the two models?
We will add the equations for the temperature and moisture scalars to the supplementary information.
L 150 What is the respiration a fraction of?
During the decomposition step involving a pool transition (e.g. from litter pool 1 to soil organic matter pool 1), a fraction of the carbon respires to the atmosphere. The remaining carbon transitions to the new pool. We will revise the text for clarity.
L 178 How long was the spinup period and how was steady state determined?
The spinup involves 200 years of accelerated decomposition (Thornton and Rosenbloom, 2005), followed by 600 years of additional spinup. Following this spinup, we check that the net ecosystem exchange (NEE) should be less than 1 gC m-2 yr-1 on average. We will provide these details in the text.
L 186 How was this adjustment done?
For each month, we add or subtract a bias factor so that the 3-hourly GSWP3 monthly mean temperature matches the site-observed, and multiply the monthly precipitation data by a factor so that the monthly sum matches the site-observed value.
L 546 ‘model’ repeated
Thank you, we will correct this error.
L 558 Missing bracket
Thank you, we will fix this.
Citation: https://doi.org/10.5194/bg-2021-163-AC2
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AC2: 'Reply on RC2', Daniel Ricciuto, 15 Mar 2022
Interactive discussion
Status: closed
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RC1: 'Comment on bg-2021-163', Anonymous Referee #1, 10 Aug 2021
Ricciuto and co-authors replicate the study of Bonan et al. 2013 looking at litter decomposition rates that are applied in two different flavors of ELM using the CTC from CLM4 and the CNT (CENTURY-like scheme) applied in CLM4.5. Nuances here changes in nutrient availability over time and parametric uncertainty associated with the CTC model.
The authors seem keen to draw distinctions between the work presented here and that of Bonan et al., (e.g. lines 87-94, line 202, and the first several paragraphs of the discussion) in an apparent defense of the CTC model. I appreciate this motivation, but it seems somewhat petty and unproductive. More, it somewhat obscures the real distinctions that should be made between this study and the previous work. I’ve listed what seem like the real differences below.
Time evolving environmental scalars are used in both studies, and therefore NOT a real difference in the work presented. Bonan et al state in their methods “We calculated daily values of CDI by linearly interpolating between the specified monthly values to replace each model’s intrinsic calculation of temperature and moisture effects on decomposition rates”. It seems that the real difference here is that:
- Bonan et al. use the same environmental scalars that were derived from site meteorological history and applied to both models with
- Ricciuto et al. use the underlying state of ELM (using GSWP3 forcing) that are adjusted to site mean state (no details provided on how this was done) and use the surface soil temperature and moisture conditions to calculate environmental scalars for CTC and CNT.
- Both approaches presumably provide similar CDI values (or environmental scalars) as stated on line 481 (see also table 6 & Fig 3 & line 481). Thus, I don’t the boundary conditions of soil moisture and temperature are meaningfully differences in the experiment design.
Model selection also is significantly different, but this isn’t really discussed until line 500 of the test. There are several features in DAYCENT that are not applied in CLM4.5 / ELM-CNT, which (I think) include the:
- bi-directional transfer of C between SOM pools,
- rate modifiers on turnover and respiration fraction (CUE) that are a function of soil texture and pH
- Flexible stoichiometry of litter and SOM pools, which should make CENTURY less sensitive to external N availability. I would assume this is one of the bigger differences between this study and results in Bonan et al., but am only speculating.
- Maybe others…?
Bonan et al used the DAYCENT structure and parameterization, but my understanding of CNT is that the model is CENTURY-like (as is CTC by representing a first order decomposition cascade), but they aren’t really CENTURY or DAYCENT (e.g. Parton et al 1988, 1998). These comments should also be reflected in Fig 1, and the discussion (Line 472).
Indeed, looking at the RMSE reported in Table 3 of Bonan et al. and table 5 from this study one might conclude that CTC doesn’t look as bad when you lower the bar and compare it to a model also doesn’t do a very good job replicating LIDET observations (CNT). I don’t think this is the intended message the authors are trying to convey.
Treatment of potential N limitation of litter decomposition is different between the Bonan study and this work, with this study using FPI passed as a boundary condition from ELM simulations. The argument in line 510 seems to be that rapid carbon turnover in CTC creates high N demand from decaying litter which is subsequently downregulated by N availability. This seems qualitatively similar to conclusions by Bonan et al. who state “CLM-cn requires severe reduction in N-optimal decomposition rates to match the observations.” Indeed, the conclusion by Bonan et al seem consistent with results reported here showing that CTC requires strong N limitation of decay rates (Fig 3), to achieve decay rates that are still faster than LIDET observations (Fig 2), and result in a higher than observed immobilization of N (Fig 4). Am I missing something, or do these results largely confirm findings by Bonan et al regarding assumptions about N limitation that seem built into the implementation of the CTC model?
Moreover, I’m not sure this assumption of strong N limitation of litter decay is well supported in the literature, a point which is notably absent from the discussion (e.g. Knorr et al. 2005, Hobbie 2008, as well as discussion in Bonan et al. 2013). Notably, the only reference cited here (Cleveland & Townsend 2006) is for soil respiration. Another paper by the same authors (Cleveland et al. 2006) found no difference in rates of leaf litter decomposition with N or P fertilization, a finding that seems more relevant to the results present here.
The parameter sensitivity test (Figs 7-9) show that parameter adjustments can make soil CTC results look more similar to the CNT simulations. This is not surprising since the models share a nearly identical structure and theoretical basis. Here the use of the soil biogeochemistry unit test outside of ELM limit the utility of this sensitivity test. Presumably parameter combinations that reduce decomposition rates closer to the observations will have cascading effects on N mineralization rates, plant productivity, and total soil C storage. I appreciate that the litter decomposition model used here may not resolve all these feedbacks, but I would be interesting to discuss how improvements to the parameters used in these soil models may feedback to the terrestrial ecosystems ELM is intended to simulate.
Finally, I would consider breaking up lengthy paragraphs in the discussion. I also found the discussion rather long and wandering, with too much of a critique of previous work and too little interpretation of the results presented in this study. Subheadings may also help provide some structure for readers and guide the development of ideas. Finally, I find it helpful when display items are referred to directly in the discussion. This is done very sparingly now.
Minor and technical points
Left and right panels should be combined in Fig 2. The point of the paper is to compare decomposition rates that are simulated by CTC and CNT decomposition cascades. Separating the results onto different panels obscures this comparison.
Line 285, why average over litter types? The LIDET study showed that within biomes litter quality strongly influences decomposition rates. This effect was communicated in Bonan et al 2013 by showing the standard deviation in percent mass remaining for each model for each biome. Their results show that the CTC approach was much less sensitive to initial litter quality than DAYCENT. I’m assuming that same is true here, but this information should also be shown on display items.
Line 310, temperature and moisture scalars do not seem to be identical for the models (Fig 3, right panels). It seems additional information about each formulation should be provided in the methods. Maybe the shape of each function can be included in the appendix.
Line 317- this seems to be the chief difference between the CTC and CNT approaches. CTC has higher base decomposition rates that are downregulated by nitrogen limitation, resulting in what the authors generally deem acceptable agreement with LIDET observations. This presents an interesting set of assumptions in the two model parameterizations that should be testable with experimental results. I know LIDET didn’t manipulate N availability, but the assumption in CTC that decomposition rates would be much larger with increased inorganic N availability does not seem well supported by N fertilization experiments that look at decomposition rates. What does this mean for assumptions and the parameterization of the CTC model?
The introduction motivates this study by the need to improve representation of soil C turnover times and stocks in Earth system models. Given this motivation would adding total soil C stocks to Table 6 be instructive? Alternatively, a plot showing NPP-SOC relationships across the LIDET sites for both models may be helpful for illustrating similarities / differences among the models?
Fig 4 should also include TRAE results, I would guess they show massive N immobilization, but should be included for completeness.
Line 175, are the underlying litter and soil C pools spun up before the litter begins? If so, how? If not, why?
What happens to L3 in the CTC litterbag. Does it decompose in this experiment, and if so, where do C fluxes go?
Line 202, this sentence is misleading since both this study and Bonan et al 2013 use a “functional unit representation”. Moreover, both studies use time evolving environmental scalars (see comment above).
Line 360, why are litter decomposition rates sensitive to the turnover times of SOM pools in CTC? After roughly year 1 of the experiment the sensitivity of litter decay is more strongly controlled by SOM turnover than turnover of litter pools (and allocation to metabolic vs. ligni litter pools is almost never important?!) Is this because you’re counting everything in the ‘litterbag layer’ including LIT and SOM pools? If so, it suggests that a bunch of material is moving into SOM in one year. This may be expected at the tropical BCI site, but it seems unrealistic at a much colder site like BNZ.
Similarly, litter decay rates even show sensitivity to the turnover of SOM4 at BCI by the end of the decade long LIDET experiment in Fig 5. I know these pools are somewhat arbitrary, but even tropical forests have SOM with old radiocarbon ages. I would assume that SOM4 is supposed to be broadly representative of this ‘passive’ soil C pool, but sensitivity of litter decay rates to the parameterization of SOM4 suggests that C, and by extension N, is really blazing through CTC in ways that may not be consistent with our understanding of factors controlling persistence of soil C stocks. This point seems warranted in the discussion of results. See also line 414, where the C:N ratio of SOM4 is important for N dynamics in the litter bag study.
Line 473, Bonan et al use DAYCENT, which is not the same as the CNT model being tested here. I’d suspect these differences reported by Bonan et al have more to do with the model structure than the experimental design.
Line 482, you’d have to check with the authors on the Bonan paper, but as mentioned above I don’t think that seasonal variation is neglected in their work.
Line 487, I appreciate that ELM assumes strong N limitation of litter decomposition, but I would appreciate references supporting this assumption from experimental manipulations of real ecosystems.
Line 491, this is true, Bonan’s work is not vertically resolved, but it’s also using DAYCENT, not the CENTURY-like approach applied by Koven et al.
Line 514, I either don’t understand or strongly disagree with this statement. This work seems to suggest that turnover rates are strongly linked to net N demand (and mineralization and immobilization). Indeed, the only two things soil biogeochemistry does in a model like ELM are store (or lose) carbon which is important for climate-carbon feedbacks, and mineralization nitrogen (controlling the magnitude of plant response to warming and elevated CO2). Both of these seem directly linked the parameterization of the soil biogeochemistry model.
Line 534, reference needed to support this claim.
Lines 535-555 this shout out to some microbial explicit models is nice, but I’m not sure how it relates to the work presented here (especially related to dynamic CUE).
Line 550, If this text stays in the revised paper see also Wieder et al. 2015, Kyker-Snowman et al. 2020.
Line 555-575. This catchall paragraph that talks about N immobilization, home field advantage, and UV degradation of litter kind of makes my head spin. Maybe break this up and bring some of the ideas back to how they may be considered in a model like ELM (if at all). Notably, it seems the N immobilization dynamic (Fig 4) seem critical to address for the use of these soil models in ELM?
Line 558, I don’t understand why you need to invoke N losses from litterbags here. Couldn’t rates of immobilization simply be too high (e.g. modeled litterbags translocate too much N from surface soils or surface soils have unrealistically high C (and therefore N turnover) that’s available for litter bag immobilization?
Line 576, I agree with this statement, but don’t see how the work presented helps us think about soil organic matter, as it’s a litter decomposition study.
References:
Cleveland, C.C., Reed, S.C. and Townsend, A.R. (2006), NUTRIENT REGULATION OF ORGANIC MATTER DECOMPOSITION IN A TROPICAL RAIN FOREST. Ecology, 87: 492-503. https://doi.org/10.1890/05-0525
Hobbie SE (2008) Nitrogen effects on decomposition: a five-year experiment in eight temperate sites. Ecology, 89, 2633–2644.
Knorr, M., Frey, S.D. and Curtis, P.S. (2005), NITROGEN ADDITIONS AND LITTER DECOMPOSITION: A META-ANALYSIS. Ecology, 86: 3252-3257. https://doi.org/10.1890/05-0150
Kyker-Snowman, E., Wieder, W. R., Frey, S. D., & Grandy, A. S. (2020). Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0). Geoscientific Model Development, 13(9), 4413-4434. doi: 10.5194/gmd-13-4413-2020.
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., Taylor, P. G., & Bonan, G. B. (2015). Representing life in the Earth system with soil microbial functional traits in the MIMICS model. Geoscientific Model Development, 8(6), 1789-1808. doi: 10.5194/gmd-8-1789-2015.
Citation: https://doi.org/10.5194/bg-2021-163-RC1 -
AC1: 'Reply on RC1', Daniel Ricciuto, 15 Mar 2022
We thank the reviewer for the detailed comments and feedback. We copy the review below with responses in bold text for each specific point.
Ricciuto and co-authors replicate the study of Bonan et al. 2013 looking at litter decomposition rates that are applied in two different flavors of ELM using the CTC from CLM4 and the CNT (CENTURY-like scheme) applied in CLM4.5. Nuances here changes in nutrient availability over time and parametric uncertainty associated with the CTC model.
The authors seem keen to draw distinctions between the work presented here and that of Bonan et al., (e.g. lines 87-94, line 202, and the first several paragraphs of the discussion) in an apparent defense of the CTC model. I appreciate this motivation, but it seems somewhat petty and unproductive. More, it somewhat obscures the real distinctions that should be made between this study and the previous work. I’ve listed what seem like the real differences below.
We greatly appreciate the perspective from an objective viewpoint and will adjust the text and tone accordingly following the more detailed suggestions below.
Time evolving environmental scalars are used in both studies, and therefore NOT a real difference in the work presented. Bonan et al state in their methods “We calculated daily values of CDI by linearly interpolating between the specified monthly values to replace each model’s intrinsic calculation of temperature and moisture effects on decomposition rates”. It seems that the real difference here is that:
- Bonan et al. use the same environmental scalars that were derived from site meteorological history and applied to both models with
- Ricciuto et al. use the underlying state of ELM (using GSWP3 forcing) that are adjusted to site mean state (no details provided on how this was done) and use the surface soil temperature and moisture conditions to calculate environmental scalars for CTC and CNT.
- Both approaches presumably provide similar CDI values (or environmental scalars) as stated on line 481 (see also table 6 & Fig 3 & line 481). Thus, I don’t the boundary conditions of soil moisture and temperature are meaningfully differences in the experiment design.
It is correct that the CDI used by Bonan et al. represents the seasonal pattern of environmental scalars. We emphasize here that we use the environmental scalars that are consistent with those that are used in simulations of ELM, the full land-surface model. We will modify the text accordingly and provide additional detail about how the environmental scalars are calculated in the updated manuscript.
Model selection also is significantly different, but this isn’t really discussed until line 500 of the test. There are several features in DAYCENT that are not applied in CLM4.5 / ELM-CNT, which (I think) include the:
- bi-directional transfer of C between SOM pools,
- rate modifiers on turnover and respiration fraction (CUE) that are a function of soil texture and pH
- Flexible stoichiometry of litter and SOM pools, which should make CENTURY less sensitive to external N availability. I would assume this is one of the bigger differences between this study and results in Bonan et al., but am only speculating.
- Maybe others…?
Bonan et al used the DAYCENT structure and parameterization, but my understanding of CNT is that the model is CENTURY-like (as is CTC by representing a first order decomposition cascade), but they aren’t really CENTURY or DAYCENT (e.g. Parton et al 1988, 1998). These comments should also be reflected in Fig 1, and the discussion (Line 472).
We agree that the implementation of ELM-CNT is significantly different than DAYCENT, and therefore we cannot imply that the results from DAYCENT are applicable to ELM-CNT or vice versa. We will add a paragraph describing the differences among these models to the methods section and revise the text in the discussion to emphasize these points.
Indeed, looking at the RMSE reported in Table 3 of Bonan et al. and table 5 from this study one might conclude that CTC doesn’t look as bad when you lower the bar and compare it to a model also doesn’t do a very good job replicating LIDET observations (CNT). I don’t think this is the intended message the authors are trying to convey.
One of the messages that we attempt to convey here is that indeed the CNT and CTC implementations are quite similar in performance. This may help to avoid confusion about the relevance of analyses based on DAYCENT for the implementation of CNT in CLM/ELM. It can certainly be argued that DAYCENT has superior results to both implementations in ELM, but these ELM approaches have been used for a number of global offline and coupled experiments. Therefore, we believe the knowledge that both approaches are similar in performance is useful for the community. Despite similar model structures, key parameters (e.g. turnover times) in the two models are actually quite different. Therefore, the similar performance was actually not something we expected to see until we performed further analysis of the nutrient feedbacks.
Treatment of potential N limitation of litter decomposition is different between the Bonan study and this work, with this study using FPI passed as a boundary condition from ELM simulations. The argument in line 510 seems to be that rapid carbon turnover in CTC creates high N demand from decaying litter which is subsequently downregulated by N availability. This seems qualitatively similar to conclusions by Bonan et al. who state “CLM-cn requires severe reduction in N-optimal decomposition rates to match the observations.” Indeed, the conclusion by Bonan et al seem consistent with results reported here showing that CTC requires strong N limitation of decay rates (Fig 3), to achieve decay rates that are still faster than LIDET observations (Fig 2), and result in a higher than observed immobilization of N (Fig 4). Am I missing something, or do these results largely confirm findings by Bonan et al regarding assumptions about N limitation that seem built into the implementation of the CTC model?
Our results do show that N limitation is an important factor regulating litter decomposition in the ELM simulations. Since this control by N availability is the intended behavior of the ELM model, the main point is that simulated decomposition rates match LIDET observations more closely than implied by Bonan et al. 2013 (their Figure 3). We will adjust the text to better motivate and describe the comparison with Bonan et al.
Moreover, I’m not sure this assumption of strong N limitation of litter decay is well supported in the literature, a point which is notably absent from the discussion (e.g. Knorr et al. 2005, Hobbie 2008, as well as discussion in Bonan et al. 2013). Notably, the only reference cited here (Cleveland & Townsend 2006) is for soil respiration. Another paper by the same authors (Cleveland et al. 2006) found no difference in rates of leaf litter decomposition with N or P fertilization, a finding that seems more relevant to the results present here.
We agree that the discussion should better address this point and we will add a paragraph to the text. One argument for the N inhibition of decomposition is the strong dependence of mass loss on litter quality observed in LIDET and other litter bag experiments, including litter N or C/N ratio independent of lignin content (Cornwell et al., 2008; Zhang et al., 2008). We will also add a figure showing the variation of mass loss with litter type and how this compares with the observed values. Fertilization studies show more mixed results. While some studies show that N addition does not strongly limit litter decay, others do indicate limitation. Knorr et al. (2005) reports inhibition for low-quality (i.e. high lignin) litters and for long decay periods. Stimulation was observed for shorter periods and for high quality litter. A recent study by Gill et al. (2021) also indicates that N fertilization accelerates decomposition of leaf litter initially, followed by slower decomposition later.
Cornwell, W. K., Cornelissen, J. H., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., et al. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071. doi: 10.1111/j.1461-0248.2008.01219.x
Gill, A., J. Schilling, S.E. Hobbie (2021) Experimental nitrogen fertilization globally accelerates, then slows decomposition of leaf litter. Ecology Letters, 24(4), 802-811, https://doi.org/10.1111/ele.13700
Knorr, M., S.D. Frey, P.S. Curtis (2005) Nitrogen additions and litter decomposition: A meta-analysis. Ecology, 86(12), 3252-3257, https://doi.org/10.1890/05-0150
Deqiang Zhang, Dafeng Hui, Yiqi Luo, Guoyi Zhou, Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors, Journal of Plant Ecology, Volume 1, Issue 2, June 2008, Pages 85–93, https://doi.org/10.1093/jpe/rtn002
The parameter sensitivity test (Figs 7-9) show that parameter adjustments can make soil CTC results look more similar to the CNT simulations. This is not surprising since the models share a nearly identical structure and theoretical basis. Here the use of the soil biogeochemistry unit test outside of ELM limit the utility of this sensitivity test. Presumably parameter combinations that reduce decomposition rates closer to the observations will have cascading effects on N mineralization rates, plant productivity, and total soil C storage. I appreciate that the litter decomposition model used here may not resolve all these feedbacks, but I would be interesting to discuss how improvements to the parameters used in these soil models may feedback to the terrestrial ecosystems ELM is intended to simulate.
Thank you for bringing up this point. A parameter sensitivity analysis for the full ELM model is useful to better understand the impact of feedbacks between litter decomposition, nutrient cycling, and vegetation feedbacks. Previous studies (e.g. Ricciuto et al., 2018) showed that these model parameters were not especially sensitive. We will update the manuscript with an additional figure showing how the sensitive parameters in the decomposition model impact the full ELM.
Finally, I would consider breaking up lengthy paragraphs in the discussion. I also found the discussion rather long and wandering, with too much of a critique of previous work and too little interpretation of the results presented in this study. Subheadings may also help provide some structure for readers and guide the development of ideas. Finally, I find it helpful when display items are referred to directly in the discussion. This is done very sparingly now.
Thank you for the suggestion. We will revise the discussion with more focus on interpreting results in the study, and break it up with subheadings.
Minor and technical points
Left and right panels should be combined in Fig 2. The point of the paper is to compare decomposition rates that are simulated by CTC and CNT decomposition cascades. Separating the results onto different panels obscures this comparison.
We will revise this figure as suggested.
Line 285, why average over litter types? The LIDET study showed that within biomes litter quality strongly influences decomposition rates. This effect was communicated in Bonan et al 2013 by showing the standard deviation in percent mass remaining for each model for each biome. Their results show that the CTC approach was much less sensitive to initial litter quality than DAYCENT. I’m assuming that same is true here, but this information should also be shown on display items.
We will revise the figure to show the variation over litter types similar to Bonan et al.’s figure, and include a supplementary figure that shows the responses for individual litter types.
Line 310, temperature and moisture scalars do not seem to be identical for the models (Fig 3, right panels). It seems additional information about each formulation should be provided in the methods. Maybe the shape of each function can be included in the appendix.
There are some differences between the models, we will include the equations for the temperature and moisture scalars in the supplementary material.
Line 317- this seems to be the chief difference between the CTC and CNT approaches. CTC has higher base decomposition rates that are downregulated by nitrogen limitation, resulting in what the authors generally deem acceptable agreement with LIDET observations. This presents an interesting set of assumptions in the two model parameterizations that should be testable with experimental results. I know LIDET didn’t manipulate N availability, but the assumption in CTC that decomposition rates would be much larger with increased inorganic N availability does not seem well supported by N fertilization experiments that look at decomposition rates. What does this mean for assumptions and the parameterization of the CTC model?
Decomposition shows mixed responses to fertilization experiments, but shows a strong dependence on litter C:N ratios (see above response). Without strong N limitation, we would only see a litter quality effect in the simulation through lignin content, but not C:N ratio.
The introduction motivates this study by the need to improve representation of soil C turnover times and stocks in Earth system models. Given this motivation would adding total soil C stocks to Table 6 be instructive? Alternatively, a plot showing NPP-SOC relationships across the LIDET sites for both models may be helpful for illustrating similarities / differences among the models?
Thank you for this great suggestion. We will revise the table to add C stocks.
Fig 4 should also include TRAE results, I would guess they show massive N immobilization, but should be included for completeness.
We will revise the figure to include TRAE results.
Line 175, are the underlying litter and soil C pools spun up before the litter begins? If so, how? If not, why?
Yes, the litter and SOM pools are spun up using a 200-year accelerated decomposition framework commonly used in ELM (Thornton and Rosenbloom, 2005), followed by a “regular” spinup for 600 additional years.
What happens to L3 in the CTC litterbag. Does it decompose in this experiment, and if so, where do C fluxes go?
L3 decomposes into SOM3. There is a missing arrow in the figure, we apologize for this error and will correct it in the revised manuscript.
Line 202, this sentence is misleading since both this study and Bonan et al 2013 use a “functional unit representation”. Moreover, both studies use time evolving environmental scalars (see comment above).
We will revise this sentence to avoid misleading the reader. Because both use functional units and time evolving environmental scalars, the main point is that our study uses time-varying nitrogen availability from the full ELM.
Line 360, why are litter decomposition rates sensitive to the turnover times of SOM pools in CTC? After roughly year 1 of the experiment the sensitivity of litter decay is more strongly controlled by SOM turnover than turnover of litter pools (and allocation to metabolic vs. ligni litter pools is almost never important?!) Is this because you’re counting everything in the ‘litterbag layer’ including LIT and SOM pools? If so, it suggests that a bunch of material is moving into SOM in one year. This may be expected at the tropical BCI site, but it seems unrealistic at a much colder site like BNZ.
This is correct, in our model the “litterbag” includes both litter and SOM pools. Given the turnover times in CTC, a significant fraction of the litter is turning over to SOM. We suspect that the base turnover rates for litter pools are relatively insensitive not because everything has transitioned to SOM, but because actual rates are a function of parameters that control N availability. In the revised MS, we will conduct additional sensitivity analyses with fixed FPI to better understand the impacts of N limitation.
Similarly, litter decay rates even show sensitivity to the turnover of SOM4 at BCI by the end of the decade long LIDET experiment in Fig 5. I know these pools are somewhat arbitrary, but even tropical forests have SOM with old radiocarbon ages. I would assume that SOM4 is supposed to be broadly representative of this ‘passive’ soil C pool, but sensitivity of litter decay rates to the parameterization of SOM4 suggests that C, and by extension N, is really blazing through CTC in ways that may not be consistent with our understanding of factors controlling persistence of soil C stocks. This point seems warranted in the discussion of results. See also line 414, where the C:N ratio of SOM4 is important for N dynamics in the litter bag study.
Given the base turnover time of ~30 years for SOM4, we do begin to see some sensitivity related to parameters controlling the decomposition of this pool at the end of 10 years. We agree that this is an important point and will add some discussion. Old radiocarbon ages generally occur at greater soil depths while our LIDET analysis only focuses on the top soil layer. For both CNT and CTC, ELM includes a depth-dependent decomposition factor which may greatly decrease decomposition rates (Koven et al., 2013) at depth. Simulated radiocarbon profiles are highly sensitive to the parameter controlling this depth dependence. We believe addressing this is beyond the scope of this study, but will look more closely at this in future work as a more comprehensive evaluation of CTC.
Line 473, Bonan et al use DAYCENT, which is not the same as the CNT model being tested here. I’d suspect these differences reported by Bonan et al have more to do with the model structure than the experimental design.
Thank you for the comment, we will revise this sentence accordingly.
Line 482, you’d have to check with the authors on the Bonan paper, but as mentioned above I don’t think that seasonal variation is neglected in their work.
We will revise this to indicate the key difference is for dynamics/seasonal dependence of N limitation, not environmental scalars.
Line 487, I appreciate that ELM assumes strong N limitation of litter decomposition, but I would appreciate references supporting this assumption from experimental manipulations of real ecosystems.
We agree that this is a very important topic for discussion. Please see the response above.
Line 491, this is true, Bonan’s work is not vertically resolved, but it’s also using DAYCENT, not the CENTURY-like approach applied by Koven et al.
Here we intended to discuss the difference between and relevance of Bonan’s implementation of CTC and ours. We will revise the text for clarity.
Line 514, I either don’t understand or strongly disagree with this statement. This work seems to suggest that turnover rates are strongly linked to net N demand (and mineralization and immobilization). Indeed, the only two things soil biogeochemistry does in a model like ELM are store (or lose) carbon which is important for climate-carbon feedbacks, and mineralization nitrogen (controlling the magnitude of plant response to warming and elevated CO2). Both of these seem directly linked the parameterization of the soil biogeochemistry model.
This sentence was worded poorly. The model is relatively insensitive to the base rates of litter decomposition, but by definition must be sensitive to the soil biogeochemistry model parameters. Our sensitivity analysis indicates that the sensitivity of actual decomposition rates over the course of the 10-year period is related to parameters driving rates of immobilization and mineralization, mainly the respiration factors (rf parameters) and SOM decomposition base rates. We will revise this sentence for clarity and expand the sensitivity analysis as suggested above to better make these points.
Line 534, reference needed to support this claim.
We will add a reference here.
Lines 535-555 this shout out to some microbial explicit models is nice, but I’m not sure how it relates to the work presented here (especially related to dynamic CUE).
This work suggests the high sensitivity of decomposition to CUE, indicating that considering dynamic CUE may strongly impact Earth system model simulations. We agree this section is too long considering we are not addressing this topic directly, and will shorten it.
Line 550, If this text stays in the revised paper see also Wieder et al. 2015, Kyker-Snowman et al. 2020.
Thank you for the suggested references, we will add them as appropriate considering the previous comment.
Line 555-575. This catchall paragraph that talks about N immobilization, home field advantage, and UV degradation of litter kind of makes my head spin. Maybe break this up and bring some of the ideas back to how they may be considered in a model like ELM (if at all). Notably, it seems the N immobilization dynamic (Fig 4) seem critical to address for the use of these soil models in ELM?
We will break up this discussion into separate topics and focus more discussion on the topic of N immobilization. In particular, Fig. 7 indicates a large range of trajectories for the fraction of N remaining, reflecting different N immobilization dynamics for different combinations of parameters. We will conduct some additional analysis of our ensemble simulations to determine whether it is possible through parameterization to reproduce both the observed C loss and the trajectory of N. This will provide more information for analysis and discussion of the appropriateness of our model structure.
Line 558, I don’t understand why you need to invoke N losses from litterbags here. Couldn’t rates of immobilization simply be too high (e.g. modeled litterbags translocate too much N from surface soils or surface soils have unrealistically high C (and therefore N turnover) that’s available for litter bag immobilization?
We will remove possible N losses from the discussion as it is not relevant to the results. The factors mentioned all may be important in addition to possible loss of C from the litterbag from leaching. We will revise the discussion to add the points.
Line 576, I agree with this statement, but don’t see how the work presented helps us think about soil organic matter, as it’s a litter decomposition study.
We will remove this sentence from the conclusion.
Citation: https://doi.org/10.5194/bg-2021-163-AC1
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RC2: 'Comment on bg-2021-163', Anonymous Referee #2, 02 Sep 2021
This paper is a modelling study looking at whether two existing soil process representations within the ELM model can capture observations of litter decomposition from a distributed litterbag experiment. The authors devise a modelling framework - a functional unit approach - to easily reproduce the large number of observations as well as our conceptual understanding of decomposition inside litterbags. The authors conclude that the two different representations have a similar capability to reproduce the observations.
The modelling approach is robust and the manuscript is well written, but I find the authors downplay their results in the discussion section. This is largely a comparison with another similar study and how their modelling approach and results differ from that of Bonan et al., 2013. I am not very familiar with that study and cannot provide a point by point comparison, but surely there is more to the paper under review than whether it does or does not produce the same results as a paper from 8 years ago? Specifically, I think it would be interesting to discuss more the implications of the results and future modelling directions. How can we improve our models? What other data should we use? Should we focus more on parameterisation than structure, as the parameter sensitivity suggests? These points are touched briefly in the last couple of paragraphs but could be emphasized more for a stronger paper.
A couple of other points that would make the paper easier to read: there are a lot of acronyms, especially related to the site names and litter types; I wonder if these could be replaced with more intuitive names, especially the litter types? E.g. high/low lignin or high/low CN? Also, the introduction could be improved by some headings to guide the reader.
Minor comments
Table 1 - It would help to add lat/lon information as not everyone is familiar with the exact location of US states.
L 146 Are the temperature, moisture etc modifiers common to the two models?L 150 What is the respiration a fraction of?
L 178 How long was the spinup period and how was steady state determined?
L 186 How was this adjustment done?
L 546 ‘model’ repeated
L 558 Missing bracket
Citation: https://doi.org/10.5194/bg-2021-163-RC2 -
AC2: 'Reply on RC2', Daniel Ricciuto, 15 Mar 2022
We thank the reviewer for the thoughtful comments. The review is copied below with resposnes in bold text.
This paper is a modelling study looking at whether two existing soil process representations within the ELM model can capture observations of litter decomposition from a distributed litterbag experiment. The authors devise a modelling framework - a functional unit approach - to easily reproduce the large number of observations as well as our conceptual understanding of decomposition inside litterbags. The authors conclude that the two different representations have a similar capability to reproduce the observations.
The modelling approach is robust and the manuscript is well written, but I find the authors downplay their results in the discussion section. This is largely a comparison with another similar study and how their modelling approach and results differ from that of Bonan et al., 2013. I am not very familiar with that study and cannot provide a point by point comparison, but surely there is more to the paper under review than whether it does or does not produce the same results as a paper from 8 years ago? Specifically, I think it would be interesting to discuss more the implications of the results and future modelling directions. How can we improve our models? What other data should we use? Should we focus more on parameterisation than structure, as the parameter sensitivity suggests? These points are touched briefly in the last couple of paragraphs but could be emphasized more for a stronger paper.
Thank you for the comments. We agree that the manuscript focused too much on the comparison with Bonan et al. and not enough on the implications of the new results presented here. We focus here on how the nutrient dynamics influence the turnover rates, and will re-organize the discussion to highlight these results and put them in the context of 1) previous studies and 2) how additional data and modeling may help to resolve model parameter and structural uncertainties. The discussion will be reorganized to emphasize these points.
A couple of other points that would make the paper easier to read: there are a lot of acronyms, especially related to the site names and litter types; I wonder if these could be replaced with more intuitive names, especially the litter types? E.g. high/low lignin or high/low CN? Also, the introduction could be improved by some headings to guide the reader.
Thank you for the suggestions. We would like to keep the litter names for consistency with previous studies, but will indicate the C/N ratios next to the litter name. We will edit the text to use more descriptive site names.
Minor comments
Table 1 - It would help to add lat/lon information as not everyone is familiar with the exact location of US states.
We will add the lat/lon information to the site table.
L 146 Are the temperature, moisture etc modifiers common to the two models?
We will add the equations for the temperature and moisture scalars to the supplementary information.
L 150 What is the respiration a fraction of?
During the decomposition step involving a pool transition (e.g. from litter pool 1 to soil organic matter pool 1), a fraction of the carbon respires to the atmosphere. The remaining carbon transitions to the new pool. We will revise the text for clarity.
L 178 How long was the spinup period and how was steady state determined?
The spinup involves 200 years of accelerated decomposition (Thornton and Rosenbloom, 2005), followed by 600 years of additional spinup. Following this spinup, we check that the net ecosystem exchange (NEE) should be less than 1 gC m-2 yr-1 on average. We will provide these details in the text.
L 186 How was this adjustment done?
For each month, we add or subtract a bias factor so that the 3-hourly GSWP3 monthly mean temperature matches the site-observed, and multiply the monthly precipitation data by a factor so that the monthly sum matches the site-observed value.
L 546 ‘model’ repeated
Thank you, we will correct this error.
L 558 Missing bracket
Thank you, we will fix this.
Citation: https://doi.org/10.5194/bg-2021-163-AC2
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AC2: 'Reply on RC2', Daniel Ricciuto, 15 Mar 2022
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