The authors present a novel approach to enhance the state-of-the-art data-driven NEE estimation system, X-BASE, by incorporating atmospheric constraints. The newly developed system, EC-STILT, addresses key limitations of X-BASE—specifically, the overestimation of the global total terrestrial sink and the underestimation of NEE interannual variability (IAV)—while retaining the valuable strength of providing fine-scale spatial distributions of terrestrial carbon fluxes, a feature often lacking in traditional inverse modeling approaches. Thus, this study is to be of broad interest to both data-driven modeling and atmospheric inversion communities. However, before publication, I encourage the authors to address several points regarding the system configuration and interpretation of the results.

Major comments

1. The authors should more clearly explain how EC-STILT, using only one year of data from each of the three atmospheric CO₂ observation sites, was able not only to correct biases in global NEE but also to more than double the global total NEE interannual variability (IAV) compared to X-BASE. In the annual regional NEE estimates, EC-STILT shows larger deviations from inversion estimates than X-BASE in regions with atmospheric constraints, such as the Eurasian Boreal and South American Tropical regions. Meanwhile, regions without direct atmospheric constraints—such as South American Temperate, Southern Africa, and Tropical Asia—show substantial increases in NEE IAV, contributing significantly to the increase in global IAV. Based on these results, it is difficult to understand how atmospheric constraints led to improved global NEE estimates and their IAV.
   
   
   
   The authors state, “This is because EC-STILT learns its land-surface response in environmental space of the features instead of in geographic space like an inversion.” If this interpretation is correct, then the neural network within EC-STILT adjusts biome-specific NEE sensitivities to environmental drivers (e.g., temperature or moisture) in a way that minimizes the loss function. For example, the model may predict stronger NEE sensitivity to moisture in tropical forests, leading to increased IAV in regions with high moisture variability. But does this sensitivity enhancement improve IAV only in some regions within a biome and not others, due to spatial heterogeneity? While the neural network may function as a black box, I believe the authors could still provide further insight based on available model outputs. For example, exploring differences in learned climate/environmental sensitivities of NEE between EC_STILT and X-BASE by regions and/or biome types could help readers better understand why the model produced the observed results.
   
   
   
   
   
   
2. It is unclear why the authors chose to use only three tall-tower atmospheric CO₂ observations, given the availability of long-term surface, aircraft, and satellite-based datasets. Was there a decrease in model performance when more observations were included? Or was the goal to test the efficiency of the system using a minimal number of atmospheric constraints?
   
   
   
   The current EC-STILT system shows substantial regional deviations from inversion-based estimates, with higher RMSE than X-BASE in some regions. While inversion estimates are not ground truth, this suggests that the information from just three sites may be insufficient to improve regional NEE distributions. Although the authors mention plans to address this in future work, it would strengthen the manuscript to provide at least a preliminary assessment—such as how results change when incorporating background in-situ measurements from NOAA’s ObsPack data.

Detailed comments

1. Line 2: The phrase "terrestrial land–atmosphere flux of CO₂" seems to refer more closely to net land flux or net biosphere exchange rather than net ecosystem exchange (NEE). I suggest using “net ecosystem exchange” to make the intended meaning clearer.
2. Lines 11–17: As noted earlier, while your study effectively reduces global NEE biases and improves interannual variability using a limited number of atmospheric CO₂ observations, it also leads to increased regional biases—assuming that inversion estimates are reasonably close to the truth. Since a broader set of atmospheric CO₂ data is available, including surface, aircraft, and satellite observations, it seems likely that incorporating more of them could improve both global and regional NEE estimates. Could you clarify why only a limited set of atmospheric observations was used in this study?
3. Lines 62–71: You mention a key limitation of the previous work by Upton et al. (2024)—that the additional atmospheric information was aggregated and provided no added value for resolving the spatial distribution of NEE. How your EC-STILT approach overcome this limitation? Could you explicitly discuss which aspects of EC-STILT (i.e., global and regional NEE estimates and their IAV) show improvement over the previous work, which do not, and what underlying factors might explain these differences.
4. Line 125: As you discuss later in the manuscript, inversion-based terrestrial biosphere flux estimates include not only fire emissions but also lateral fluxes. Since your study assumes that NEE corresponds to the inversion estimate with fire emissions removed, it would be helpful to state this assumption clearly at this point in the text.
5. Line 176: Please provide more detail on how the lateral boundary conditions for the region are derived from the 3D CO₂ fields provided by Jena CarbonScope, and how these are applied in Equation (1).
6. Lines 205–206: The way uncertainty is defined and prescribed seems to be a critical component of your system, but the explanation provided is not sufficiently detailed. Could you clarify how uncertainties were defined in your framework, and how the relative weighting between atmospheric constraints and eddy-covariance observation constraints was determined?
7. Lines 233–234: The phrase “with only local driver variables, and no atmospheric information” is somewhat unclear. It would be helpful to revise this sentence to more specifically describe what is meant by “local drivers” and “no atmospheric information”.
8. Figure 4: Please consider adding a panel showing the annual mean NEE from X-BASE, so that readers can directly compare it with the EC-STILT results. Additionally, for the panel showing the difference between EC-STILT and X-BASE, it would be helpful either to adjust the colorbar style or to use the same colorbar range as in Figure 4A to facilitate visual comparison.
9. Table 1 and Figure 5: Some values in the text, table, and Figure 5 are inconsistent. Also, bold formatting in Figure 5 seems to incorrectly indicate better performance in some cases—for example, the annual RMSE for Australia. Please review and correct these issues.
10. Lines 291–292: The statement “EC-STILT has modified its response by biome” should be supported by a clearer explanation in the Methods section. Does this mean that the relationship between driver variables and NEE is trained and applied in a biome-specific manner? If so, please clarify how this is implemented.
11. Lines 292–293: The sentence “When IAV is broken down by month (Fig. 7) across boreal and temperate regions, the increases in monthly IAV occurs during the growing season while in tropical regions show a change during the dry season” is interesting. Could you provide a potential explanation for this pattern, or at least discuss possible mechanisms that might be driving these seasonal differences across regions?
12. Lines 300–303: Could you clarify which figure or table supports this part of the text?
13. Figure 8: Some of the values shown in the figure do not match those reported in the main text. Please review and correct these inconsistencies.
14. Line 330: Since the concept of “environmental response” is a central and novel aspect of your system, it would be helpful to provide a more detailed explanation of how this is defined and implemented in the Methods section. This would help readers better interpret your results.
15. Lines 345–347: Table 1 suggests that the limited atmospheric observations used in your study are not sufficient to effectively embed the atmospheric signal into the EC-STILT ecosystem response or to guide the spatial and temporal distributions of the estimated NEE.
16. Lines 379–381: It would be helpful to include supporting results for this statement maybe in the supplementary material.