The following is my review of the paper entitled ‘Remote carbon cycle changes are overlooked impacts of land-cover and land management changes’ by Guo et al., submitted to Earth Science Dynamics. In this paper, the authors have studied the local and non-local impacts of land cover change and its management on climate variability. The warming effects are classified into biogeophysical (BGP) and biogeochemical (BGC) categories, and multiple Earth system models are used to simulate these changes for afforestation and agricultural expansion with and without irrigation. The BGC impacts are found to be significant and should be considered in impact assessment and relevant policymaking. For the irrigated cropland expansion scenario, the cumulative impacts of local and non-local BGC effects will become comparable in the future. Overall, this work is relevant to climate research, has novelty and has been conducted extensively. However, the article is written in a manner that is hard to understand, and it requires several other improvements, as I listed below. 

Specific comments:

1. This paper is very complicated to comprehend, primarily due to its structure and writing style. The descriptions of experiments and corresponding model simulations are tough to understand. There are a lot of differences among the different model experiments and their simulations. There are multiple variables considered and their interrelations. Consequently, there are multiple figures (seventeen, including the ones in the appendices, and each with multiple subplots!). However, these are not explained in sufficient detail. This makes the results inconclusive. Most of the interpretations are not clear and some of those are speculative in nature.
2. Perhaps it will be helpful for the readers to categorize the biogeophysical and biogeochemical effects into local and nonlocal categories and represent this information in a table.  
3. How the effects are separated into local and non-local is not objectively defined. Did they use any preset definition of the ‘area of influence’ or did they use a specific number of neighboring points (pixels) for such classifications?
4. Lines 59-61: I do not totally understand this statement. Changing a forest to grassland will reduce its carbon sequestration, which will increase atmospheric warming. This is a global effect and biogeochemical in nature. On the other hand, an increase in albedo will impart a cooling that is local and biogeophysical in nature.
5. Lines 130-135: The second advantage of not considering the plausible realizations in the modeling experiments is not clear.
6. CROP scenario in Table 1: So, here, basically, two land use types are considered: cropland and bare soil, right? And both these occupy 50% of each of the land grid cells? Are different biophysical and biogeochemical parameters used for the different crop types, such as albedo, rooting depth, etc.? 
7. IRR scenario in Table 1: Since all the croplands were irrigated, did the authors check the hydrological budget and ensure its closure globally? Also, what kind of irrigation was considered, fed with surface or groundwater, drip or canal, etc.? These details are required.
8. Lines 160-162: See my earlier comment on the water budget.
9. Section 2.2: The recipe used here to differentiate between the local and non-local effects is not clear. Can the authors show a flowchart summarizing these?
10. Lines 236-241: Why are changes in surface roughness due to land cover change and its impact on aerodynamic conductance not considered?
11. Figure 2: Please improve the description of different components of the terrestrial carbon cycle simulated by different models. In the present format, it is utterly confusing for the readers. For example, what about the blue dashed line? To interpret this the reader has to read the caption. Why then some other lines are described in the figure itself? Use a uniform and detailed description and present those in a way straightforward to understand. 
12. Figure 3: How are the boxes selected in panel (a) for calculating the regional averages? The boxes over northern America and Australia include oceans as well, which should not be counted in the terrestrial carbon cycle. Why don’t the authors use appropriate shapefiles to select and crop the regions of their interest? 
13. Can the authors show spatial trends of different carbon pools for different experiments and models over the simulation period for the aforementioned regions?
14. Consistent scales should be used across the subplots in all the figures. For example, the scales on the y-axis are different in the subplots of Figure 2. Similarly, the x-axis scales are different in panels (d), (h) and (l) in Figure 3. This makes the intercomparison difficult.  
15. Lines 379-383: Shouldn't the ToE for the forested regions should be slower than croplands and grasslands, since the total biomass content is significantly higher in the former land use type?
16. Figure C1: What do the panels j and k stand for? This should be described in the figure caption.
17. Several references cited in the text are missing from the bibliography, such as Arora et al.

Technical corrections:  

1. Line 165: "are shown" instead of "is shown".
2. Figure 5: CESM is marked blue; however, in the caption, it is written as red.
3. Lines 467-483: This is a big paragraph. Consider breaking it into two or more.

In this manuscript, Guo et al. explored, for the first time, the global nonlocal biogeochemical (BGC) effects of large-scale land-cover and land management changes (LCLMCs) by conducting three sensitivity simulations using three Earth system models (ESMs). The primary goal of this effort was to assess the nonlocal BGC effects resulting from the biogeophysical (BGP) effects of large-scale LCLMCs on different terrestrial carbon pools. Three global ESM-based sensitivity simulations include cropland expansion without irrigation (CROP), cropland expansion with irrigation (IRR), and afforestation (FRST). The difference between the control versus sensitivity simulations was considered to be the nonlocal BGC effects of LCLMCs. The authors found a substantial global impact of the LCLMCs on a timescale of 10-40-160 years of simulation period on different spatial scales. The nonlocal BGC can exceed the total LCLMCs effects under the IRR scenario.

This is a timely effort examining the nonlocal BGC effects of LCLMCs using ESMs. The study highlights the importance of nonlocal BGC effects of LCLMCs, which are currently neglected in scientific literature. Therefore, this paper is within the scope of the Earth System Dynamics (ESD) journal and investigates an important research question. The global ESM simulations were conducted, and nonlocal BGC effects were assessed for the first time. Therefore, this study is novel and can stimulate further studies in the same direction. This manuscript can definitely be accepted by ESD after addressing the comments below.

Main comments

Although this is a first attempt to study the nonlocal BGC effects using model sensitivity simulations, a model ensemble involving multiple ESMs, would have been more appropriate for the study. Model ensemble mean and spread would add more insights into the effects of LCLMCs. Authors can consider this aspect.

There are noticeable differences in the simulated nonlocal BGC effects among three ESMs, reflected in spatial maps, latitudinal means, and sub-regions. This is expected; however, separate sub-sections within “Results” sections should be devoted to explaining the differences (on each aspect considered).

The whole analysis of the sensitivity simulations can be improved by avoiding multiple spatial maps and focusing on latitudinal means (e.g., Figure 3 d, h, l) and sub-regional contributions (e.g., Figure 5, 6).

The abstract and conclusion sections should contain some quantitative statements: the magnitude of nonlocal BGC compared to total effects (%) on different regions, impacts of temperature on BGC effects, etc.

The definition and discussion of local versus non-local and BGP versus BGC were described in detail in the introduction section. The first 4-5 sentences of the abstract can be rewritten to comprehend these aspects concisely. An additional table (in addition to Figure 1; a nice figure!) can be included and discussed in the introduction section itself to make these definitions clearer to the readers.

The specifics of the three models (and their differences, e.g., use of LPJ in EC-Earth) are explained in detail in the methods section (Sect. 2.1). It would be better if the authors added a table listing three model details that are important for the analysis in this study.

Better assigning different colors to different models in Figure 3 d, h, l (latitudinal means) to distinguish between color scales in the spatial maps.

The naming of different regions (Figures 3a and 5) should be more careful: North America to Eastern NA, Northern Australia, etc.

It is better if the authors are consistent while describing the three scenarios in the following order: CROP, IRR, and FRST, throughout the manuscript.