| 24 Mar 2026
Impacts of Shrub Coverage for Arctic Ecosystem Carbon Uptake and Storage
Abstract. Although shrubs employ distinct water- and carbon-use strategies compared to trees and are increasingly expanding across warming tundra and grassland, they remain insufficiently represented in global land surface models. Here, we incorporated two shrub types, deciduous and evergreen, into the nutrient-enabled terrestrial biosphere model QUINCY, which features a state-of-the-art treatment of soil nutrient dynamics and carbon exchange. We investigate the change in carbon fluxes and storage due to shrub cover, it's response to climate and CO2 fertilization effect and the role of nitrogen availability. With this new implementation, shrubs showed reasonable seasonal cycle of gross primary production (GPP) at 50 % of the Arctic study sites. The model achieved mean R2 values of 0.5 and 0.6, when compared with in situ measurements and remote sensing products for modeled shrubs. However, at 50 % of the study sites the model underestimated observed GPP due to too strong simulated nitrogen limitation. Compared to needle leaved evergreen forest the modeled gross primary production of shrubs is similarly distributed with a non-significant difference in the median. Compared to graminoids the carbon fluxes of shrubs are 40 % higher. Shrubs produce a substantial, though lower, above-ground biomass than needle leaved trees and show phenological patterns that are distinct from those of trees. Although CO2 fertilization generally benefits all plant types, shrubs appear to maintain a particularly strong growth response under elevated CO2 concentrations. We also demonstrated that the modeled deciduous shrubs reduce their nitrogen sources substantially more than evergreen shrubs, generally resulting in a 50 % decrease in gross primary production. Providing the plants with unlimited nitrogen and thus doubling gross primary production at most sites improved the model-measurement agreement by 15 %. A similar effect occurred when initializing nitrogen and carbon contents best on permafrost profiles, resulting in partly alleviating nitrogen limitation in the model. These finding underlines the importance of including evergreen and deciduous shrub PFTs in global land surface models to accurately predict ongoing changes in the Arctic carbon cycle. However, the strong nitrogen limitation of Arctic shrub productivity when using the standard model parametrizations suggests that the Arctic contribution to global land carbon is underestimated by global models.
GENERAL COMMENTS
This manuscript adds explicit evergreen and deciduous Arctic shrub groups to the plant functional types included in the QUINCY climate model in an effort to address differences between models using just trees versus including shrubs in three areas: carbon uptake, CO2 fertilization and warming effects, and nitrogen cycling (particularly in reference to permafrost, which is preliminarily added to the model). The study consists of two main parts: implementing and validating the new shrub PFTs into the QUINCY model and examining the shrub-specific carbon and nitrogen cycling based on the model. The authors find strong correlations between modeled and observed productivity data and recreate fertilization and nitrogen dynamics found in other studies, demonstrating their success in adding shrub PFTs to the QUINCY model as a basis to address further questions, though there is less evidence that the model is directly improved by the new PFTs versus the existing ones. As there is no new measured data and the manuscript hinges on the QUINCY model results, without a convincing improvement in the model, the scientific content is limited to measuring shrub-specific carbon and nitrogen cycling. The title of the work suggests the authors also focus on those sections, but there are several statements that would be overextended based on the actual content of the paper without demonstrated model improvements (e.g. lines 16-17 "underlines the importance of... shrub PFTs in global land surface models..."). While this work could be an important step to improving a strong model linking Arctic shrub processes and climate dynamics and the science has merit, one of the major arguments made is unclear, weakening the impact. If the explanation of the impact on the model and the clarity of arguments were to be improved, the manuscript would be a valuable contribution to Arctic vegetation-climate modeling and a foundation to include more detailed permafrost dynamics to vegetation models. Any issues with the paper are not in the science but in how it is conveyed and all the most significant issues occur in trying to make general statements about the model. As written, the major scientific contributions of the study are the creation of a tool to evaluate shrub-specific climate-vegetation interactions in the Arctic and using that tool to specifically analyse how shrub growth and carbon cycling are impacted by nitrogen availability.
SPECIFIC COMMENTS
In terms of specific issues, one of the primary examples of lack of clarity is the improvement of the model by including shrubs versus using QUINCY's inbuilt PFTs. While the title of the paper focuses on the specific carbon and nitrogen impacts (questions 2 and 3), about half the paper is devoted to the implementation of the new shrub PFTs and the abstract ends with a claim that introducing shrub PFTs improves the accuracy of models. Given that, when compared, the median GPP values and total carbon between shrubs and trees are not significantly different, it would be worth emphasizing other information to show an improvement in model performance (e.g. Is the R2 improved? Are there verifiable site-level differences?, etc.). Adding a line for the model using the tree PFT to Figure 1, for example, would add weight to the significance of the paper if the R2 is improved. Since the skewness is different between shrubs and trees, does the shrub skewness better match the observed values than the tree skewness? While it is important to include the similarities in the medians, there are other statistics that can be compared to highlight what does change. Or in the other case, if measures of model accuracy are included purely for validation and do not make any statements about the general QUINCY model, it would be worth being more specific about that; even just changing a section title could improve clarity (e.g. 3.1 Validation of modeled vegetation productivity). One of the main strengths of the paper is to serve as a foundation to expand the work and extend the new model, and if that strength were explicitly conveyed, it would focus the reader on what was improved rather than the overall performance of the model.
Another key point that could be made clearer is the statement in lines 8-9, 165, and 312-313 that model nitrogen dynamics may be overly restrictive. This is a major point in the paper that relates to stronger/weaker correlations between modeled and observed data, even being referenced in the abstract, but the logic in reaching this conclusion is somewhat murky. This is not the only possible explanation for why the C-only model had higher R2 values than the CN model in half the sites, and while line 313 suggests issues in modeled nitrogen fixation, any discussion thereof is in the section on CO2 fertilization and is decoupled from the statement. For the actual logic used to reach this conclusion, to the best of my understanding, in addition to the biological nitrogen fixation mention the paragraphs about permafrost dynamics following lines 312-313 are meant to serve as an example: the reduced nitrogen limitation in the permafrost model had a stronger correlation with observed data. If this is true, the link between the two pieces of information is broken by how the next paragraphs are structured, discussing model parameters before any mention of model improvement. It is also handled far too late in the manuscript given where the claim is first introduced. Because the paper uses a "Results and Discussion" section rather than breaking them up, data and interpretations are interspersed. This means any claims need to be explained when introduced and any callbacks to other statements need to be explicit.
A technical issue with clarity is the way the supplemental material is and isn't referenced. There are 18 supplemental figures (compared to 6 in the main body and 2 in the appendix), some of which have more bearing to the actual contents of the paper. The most problematic instance is in lines 204-205, in which the reader is instructed to see Supplemental Figure 3 for R2 values that are referenced later in the paragraph (line 209: "might explain the low correlation") but are never summarized or listed in the main text, which could arguably be seen as a violation of the policy that supplementary materials cannot present findings or interpretations beyond the contents of the manuscript. As one of the review criteria is whether the quantity and quality of supplementary materials are appropriate, I would recommend reviewing the figures and determining if any of them are unnecessary (e.g. is the ubiquitous atmospheric CO2 graph in S12 needed?), are needed to be summarized or included in the manuscript (e.g. S3 R2 values), or need more information in the legend or caption (e.g. grey and black lines in S14), particularly since the code and data are available.
TECHNICAL COMMENTS
Other, smaller issues are primarily concerned with readability. There is a missing citation in Table 2 and the y-axis on Figure 5a is unlabeled but could represent portion of the month included in the growing season, biological activity in the month, portion of years in which the growing season extended into the month, or something else, changing the meaning of the figure. There is a lack of consistency in patterns between panels and between figures/tables. In Figures 2 & 3 (as well as some supplemental figures), the color representing shrubs and graminoid values swaps between panels a and b. The order in which sites are listed changes in every table and most figures. In both cases, maintaining consistency would increase the readability of the paper. In addition, Figure A1 provides important enough context that it may warrant inclusion in the main body of the manuscript, but if not, some explanation of the site codes needs to be present outside of the appendix. Lines 205-206 compare evergreen shrubs to shrubs, which is confusing - the authors may mean trees (based on context) or may mean deciduous shrubs - either way it should be clarified. The values in the paragraph in lines 219-222 may be swapped as the aboveground total is approximately twice the presented overall biomass. The NaN in Neon Healy vegetation type in Table A2 merits an explanation in the caption (similar to the Table A3 caption). The paragraph at lines 113-124 repeatedly cites the same dataset and would be better served by a statement like "using the methods of Palmtag et al. (2022), [Values used]..." to not appear more reliant on the source than the paper actually is. The manuscript would benefit from detailed copy editing as there are several small grammatical and punctuation errors, some (e.g. a missing comma after "yet" in line 249) that impact meaning, while most (e.g. wrong "its" in line 5 or "foilage" instead of "foliage" in line 272) do not.