the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ELM-TAM: a structure-based, function-oriented land model embracing fine-root system complexity
Abstract. Land models typically represent the entire fine-root system as a single homogeneous pool, collapsing structural and functional complexity into one set of parameters. This simplification weakens the belowground feedbacks that balance source-driven carbon dynamics. Here, we integrate the TAM (Transport and Absorptive fine roots with Mycorrhizal fungi) framework into the E3SM Land Model (ELM), replacing the single fine-root pool with three pools — transport roots, absorptive roots, and mycorrhizal fungi — defined by their roles in resource conduction, nutrient acquisition, and symbiotic exchange, each governed by pool-specific C : N ratios, longevity, allocation coefficients, and litter chemistry (21 new parameters). Sobol sensitivity analysis at 13 FLUXNET sites spanning 13 natural PFTs in ELM reveals a two-strategy parameterization dichotomy: deciduous and grass plant functional types (PFTs) concentrate variance in 2–3 allocation parameters, while evergreen PFTs distribute sensitivity across 5–6 parameters spanning chemistry, longevity, and allocation. Surrogate-assisted Bayesian calibration produces emergent fine-root properties that diverge from ELM defaults, with system-level nitrogen demand increasing at 10 of 14 PFTs (up to 4.3 ×) and absorptive-root longevity spanning 0.5–3.5 yr. A three-tier validation framework—monthly evaluation, temporal out-of-sample, and spatial out-of-sample—demonstrates that these changes yield transferable improvements: improved seasonal amplitude fidelity (mean GPP amplitude ratio 0.99 vs. 1.38 for the baseline), near-elimination of ER bias at a deciduous site (MBE from + 515 to + 33 gC m⁻² yr⁻¹), and cross-continental parameter transfer for C4 grasses (81 % RMSE reduction). Degradation at evergreen sites where the baseline overestimates productivity identifies a scope boundary where a belowground-only improvement cannot remedy upstream source-side bias. The function-oriented design provides natural coupling points for future extensions, including mechanistic root–water interactions and depth-resolved root profiles. ELM-TAM demonstrates that resolving belowground structural heterogeneity strengthens sink-based controls on the carbon cycle without overparameterization, establishing a foundation for these extensions and for global benchmarking in Earth System land models.
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Status: open (until 30 Oct 2026)
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CEC1: 'Comment on egusphere-2026-2759 - No compliance with the policy of the journal', Juan Antonio Añel, 08 Aug 2026
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AC1: 'Reply on CEC1', Bin Wang, 08 Aug 2026
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Dear Juan,
We appreciate the speedy review, and sorry for missing the required appropriate code deposition. Now we updated the earlier Zenodo repository with the ELM-TAM model code and the scripts for model analysis underlying this manuscript. These can be now accessed at https://zenodo.org/records/21853913.
Please let us know if you have issues with this. If so, we will address them promptly.
Best regards,
Bin Wang on behalf of all co-authors
Citation: https://doi.org/10.5194/egusphere-2026-2759-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 09 Aug 2026
reply
Dear authors,
Many thanks for the quick reply. I have checked the repository and we can consider now the current version of your manuscript in compliance with the code policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2026-2759-CEC2
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CEC2: 'Reply on AC1', Juan Antonio Añel, 09 Aug 2026
reply
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AC1: 'Reply on CEC1', Bin Wang, 08 Aug 2026
reply
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RC1: 'Comment on egusphere-2026-2759', Anonymous Referee #1, 17 Sep 2026
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The paper is very well written and represents an important development in land models.
I like the virtual experiment approach. The discussion is addressing both the improvements and the problems, which is good.
It is deeply disrespectful for the providers of the data that you are using that you do not properly reference them. Ameriflux and FLUXNET (where you got the data) provides exact instructions for how to site flux site datasets. Each site’s DOI must be referenced (Author, year) in the main text (not only an appendix, though you did not do it even there) and the full link to the DOI should be listed with the full reference, in the reference list of the main article. Similarly, please add the references to the datasets you used for forcing (listed in L473-476).
A GitHUB link is not a persistent publication source for the code. The code developed here must be made available to readers, and the GitHUB is an effort towards it, but the exact version developed here, that includes the optimization engine should be frozen and published with a DOI (Zenodo has an easy link with GitHub). Simulation setup files should be included as well.
L125 - Expand TAM on first use outside the abstract. A somewhat deconstructed expansion is listed in the next two rows, but that confused me. Listing it as you do in the abstract will make it easier to understand.
L141 – “Here we present ELM-TAM” - as far as I can gather from Wang et al 2023, TAM was already coupled with ELM there. Please be clear about the novelty presented here. Did you improve the coupling? Or, are you using the ELM-TAM from Wang et al 2023 and the novelty is the extensive sensitivity analysis and parameterization?
L196 21 parameters per PFT. Totaling 21*14 = 294.
Fig 2 – use different text color to indicate which flux sites are for calibration and which for validation
3.1.1 Surrogate model construction – replacing the model with a ML surrogate representation for parameterization seems like a big leap. Not saying its wrong, but curious if it was previously tested and published (hopefully, but then – add references and establish what is tested before and what was new here). If not (i.e., this is the first time this is introduced), little more details and little more direct validation results of this approach are needed to establish it. You have some of that in the appendix, but the highlights of that should be referenced in this section.
L523 – edit the equation as equation
Fig 6 – some sites show rather poor model performance, and yet r is very high (>0.8). Clearly, you need to report the bias or slope of the mod-obs curve as part of evaluation criteria.
Finally, I have never seen it discussed in the land model or ESM context, and the author are free to completely ignore this comment, but someone should consistently address added complexity with each new module development in an AIC framework. At what point adding more parameters becomes overfitting?
Citation: https://doi.org/10.5194/egusphere-2026-2759-RC1
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M. Luke McCormack
Daniel M. Ricciuto
Xiaojuan Yang
Min Xu
Forrest M. Hoffman
Plants send about a third of their captured carbon below ground, to roots and soil fungi that take up water and nutrients. Most climate models still treat the entire root system as one uniform mass. We built a new land-model version that separates roots into three types: transport roots, absorptive roots, and fungal partners. Tested at twenty sites worldwide, it cut errors in simulated ecosystem carbon exchange by more than half, sharpening projections of how land will respond to climate change.
Plants send about a third of their captured carbon below ground, to roots and soil fungi that...
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have archived your code on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo.
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Later, if the Topical Editor decides to continue with the review or publication process of your manuscript and you are requested to upload a new version of it, then The 'Code and Data Availability’ section of your manuscript must also be modified to cite the new repository location, and the corresponding reference added to the bibliography.
I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in GMD.
Juan A. Añel
Geosci. Model Dev. Executive Editor