Overarching comments:

This paper address water-carbon coupling in models leveraging soil-moisture sensitivity runs through the LFMIP experiments. The topic is important to the modeling community, and the work presented here is thorough and yields many good insights. I congratulate the authors on pulling together what is clearly a lot of work. However, this was a difficult read and left many points of confusion; I suggest there is room to further develop the presentation for clarity, primarily through 1) the inclusion of clear guiding hypotheses/questions that motivate and structure the analysis; 2) more consolidation of uncertainty information in the figures; and 3) clearer organization of information throughout.

Some specific comments are below.

L 30: Please define the sign of land source/sink in this terminology. For example., “The net carbon exchange on large spatial and temporal scales is referred to as Net Biome Production (NBP), where positive values indicate a net flux of carbon from atmosphere to land (sink) and negative values indicate a net flux of carbon from land to atmosphere (source)."

L 55: Although later in the paper (L 130) the focus on GPP over respiration is asserted, it is worth mentioning the important and variable impacts of SM on microbial respiration, CO2/CH4 partitioning, etc, as the context is set in the introduction. This is a major part of the hydrological feedback puzzle especially at high latitudes where, in addition, total soil C is highly uncertain.  

L66-75: Please introduce some hypotheses or questions here that can be addressed. This will make the results section far easier to read and greatly improve the presentation.

L 85: Please provide a) some brief acknowledgement that there’s no water budget closure when SM levels are prescribed as such and b) brief explanation on why the logic and feedback physics of these sensitivity experiments nonetheless works for the variables of interest.

L125: It will be clearer if the content of this sentence can be reformatted as equations:

 “The experiments of LFMIP allow isolating the effect of SM trend and variability, where ∆NBPSM trend =

∆NBPrmLC−pdLC and ∆NBPSM var = ∆NBPCT L−rmLC , as well as the combined effects of SM expressed as ∆NBPSM all =

∆NBPSM trend + ∆NBPSM var = ∆NBPCT L−pdLC.”

L145: If focusing on total SM, the methods set up can be simplified for the aid of the reader by limiting description/equations to just the pdLC and CTR experiments.

L175: (related to comment on L145) Yet, if there is substantial discussion of SMtrend and SMvar, the authors should leave these definitions in. But then I would rephrase this to say that the other results are primarily in the supplement but discussed throughout.

L184-186: negative signs are redundant with phrasing: “reducing by…”

Figure 1: When suggesting a comparison to Green et al 2019 figure 1, please provide more direction on what comparison should be made. For instance, is Figure 1 panel b meant to be a reproduction (over a different time period) of data in Green et al 2019 figure 1? If so, this is confusing, as the black, blue and pink lines do not have the same decadal-scale dynamics—this paper’s lines reach a minimum in ~2020 while Green et al 2019 lines have an approximately monotonic increase. What accounts for the difference, how should we understand this comparison?

L 200-210/figure 3 (also applies to figure 2): This analysis would be enhanced by presentation of uncertainty in the figures as follows: a) in the model-specific plots, some indication on where the trends are insignificant with stippling or shading. Without this it is hard to assess in z-score space what is going in in the Sahara—some of the Sahel SM increase in CMCC-ESM2 may be “real” accompanying Sahel greening, but some of the other widespread changes over the desert (e.g. IPSL-CM6A-LR) may be noise. Similarly, b) some indication in the ensemble mean plots where there is sign disagreement within the 4 models would greatly enhance the interpretation of the visuals. The IPCC reports have good inspiration on how to make these multi-map plots more useful by conveying this type of uncertainty information.  

L 256-265: This is not adequately interpreted. A) It would be helpful to state the hypotheses guiding this analysis. Related to that, B) to help us understand a statement such as “the results indicate that about 70–90 % of intermodel difference can be explained by either changes in the direct and indirect SM effects or the sensitivity of GPP to those effects” please list out what the possible contributors are, and how this relates to the guiding hypotheses. When I dig back to Eq4 and line 156, this statement appears to suggest that 70-90% of the effects are accounted for by “everything” which is a trivial conclusion. Please make this clearer for the reader. C) Please correct North South America D) 263-265 suggest to put this sentence higher in the paragraph, this is where the real conclusions are.  

Overall discussion: This discussion has several good insights but more organization of information is needed to make this compelling and easy to digest. For example, I could see a header “Are the models fit for purpose?” and another one “Are the experiments fit for purpose?” to help frame the current discussion of model process richness and drought impact performance and the inclusion of SM extremes, respectively, alongside some indication in the opening discussion paragraph that the authors intend to explore these ideas? This is just an example to illustrate the suggestion of how to organize the information, the authors can of course approach this how they want.

 L 276-277: “The latitudinal NBP of the northern mid–latitudes accounts for about 85 % of global NBP for the CMIP5 MMM.” Can this be rephrased? I don’t know what is meant here.

L 281-294 is informative but needs to be contextualized—currently the authors make a strong case these models are not fit to capture the key feedbacks. Including an objective model evaluation such as an ILAMB broadscore plot would help readers understand “who” these models are. The authors mention that the GFDL model has the best lagged responses, but does it outperform the other models on the benchmarks? This also pertains to the discussion on L323-333: The authors mention that CLM5 is the most process rich in the plant hydraulic space, but does it outperform other models in the benchmarks? 

Comments on “Soil moisture-induced changes in land carbon sink projections in CMIP6” by Gabel et al.

In this study, the authors compared the effects of soil moisture on GPP across CMIP5 and CMIP6 models. They found that SM variability is the main driver of land carbon reductions, and SM-atmosphere coupling is a critical factor for predicting future land carbon uptake. This research is complex and interesting, the manuscript is well-written, and suitable for the journal. The authors have put in a lot of work to complete the manuscript. However, there are some major concerns. Since this paper conducted a similar analysis to Green et al. (2019) and the main findings are similar, it is unclear what the innovative part of this study is. Secondly, the analysis is comprehensive and thorough, but missing uncertainty analysis, especially for the spatial maps (only shows the mean, without a uncertainties map). Thirdly, the method section needs significant improvement, especially regarding how the selected model simulates soil moisture, GPP, and how temperature affects GPP. Moreover, the authors did not explain whether the soil biogeochemical component of the model is included, as it is also an important part of carbon cycling. Did these models have the soil biogeochemical part? How will soil moisture affect microbial activity and Nitrogen availability? There is also a lack of a mechanism explanation for findings, please try to explain the mechanisms. Lastly, there are so many acronyms in the manuscript, which makes it very difficult to follow. Suggest making a list/table of the different acronyms. As a result, I would like to suggest a major revision.

1. Make a clear table showing the relationship between LFMIP and GLACE-CMIP5, as many acronyms for models or projects are making the manuscript very hard to read.
2. Line 30: The concept of NBP is a little unclear, please elaborate more of this concept and what is the difference between NBP and NEP?
3. Line 35: temperature-induced increases in respiration, plants or soil microbials?
4. Line 50: Does this research include the below-ground soil-biogeochemistry or only the plant-vegetation part?
5. Line 57, this part is unclear, by further reducing SM, through what feedback loop? Higher transpiration or higher evaporation? Higher VPD may also cause a close of stoma to limit water loss, so it still decreases transpiration which may not reduce SM.
6. Suggest moving table S1 to the main text and making the ESM the first row instead of using the modelling group as the first row. What is the land model resolution? How many soil layers does each model have?
7. Line 100, do not understand this paragraph, what is the purpose of selecting one ensemble member? Did the authors already select four models in the previous description?
8. Line 105, how is the NBP calculated?
9. Figure 1, there is a mismatch of caption descriptions and figure panels, please check and revise. The label of y-axis is a little confusing.
10. Line 181, “reduces the global land carbon sink to half”, this sentence is confusing and difficult to understand.
11. Figure 1, why do you choose 20 20-year centred rolling mean and your analysis period is 29 years
12. Figure S4 is never mentioned or explained in the main text.
13. Lines 205 – 210, the spatial analysis shows that the large SM differences between CMIP5 and LFMIP for the IPSL and GFDL models. Can you explain what is causing the differences. Moreover, if the ensemble of many models means kind of similar, this indicates the IPSL and GFDL may not be typical representative models, so why do you select these two models?
14. Figure 2 and 3 may need to perform some uncertainty analysis of the spatial patterns.
15. Lines 212, be specific, how many available models? Please also describe this more clear in the method section.
16. Figure 5, please include the meaning of GPP_R in the caption too. Please also explain in the text why the indirect effect R from SM leads to an increase in GPP. Can the selected model capture the optimum temperature effect for plant GPP.
17. Lines 315-320, please be specific, “some land surface models” which models? Maybe in the method section describe a little how the GPP and soil moisture are modelled in the four selected models.
18. Line 369: How to quantify the SM variability quantitatively?