The authors developed a regional model that considers the variable DOM and POM stoichiometry by incorporating preferential remineralization of organic matter and DOM release to study the impact of these processes on the carbon cycle in the Northwest European shelf seas. The manuscript is well-written, the model results align well with observations, and the discussion of the carbon cycle and the influence of variable organic matter stoichiometry is comprehensive. This study is a good example to show the importance of including variable organic matter stoichiometry in the modeling work. After some minor revisions, the manuscript can be considered for publication.

Specific Comments:

Line 125-130: did the authors consider the river input of DOC into the North Sea in the model?

Line 181: the authors assumed a 60% POM and 40% DOM separation for the fate of new detritus (this percentage can vary a lot in the ocean). Have other percentages been tested? It would be helpful to discuss whether varying this ratio might influence the conclusions of the study.

Line 261 and Fig 3: In addition to preferential remineralization and DOC release, DON or DOP direct uptake by phytoplankton is another potential pathway contributing to deviations from the Redfield Ratio. Can this pathway (DON or DOP direct uptake) be incorporated into the model? Or at least, please acknowledge this pathway and explain why it is not included in the model.

Line 296: why in the fourth configuration (EP&PR), the contributions have been reduced?

Line 334: for costal DOC, DON and DOP concentration data, it might be worth looking at CoastDOM v1 database. It collects many coastal DOM concentration data.

Lønborg, C., Carreira, C., Abril, G., Agustí, S., Amaral, V., Andersson, A., ... & Álvarez-Salgado, X. A. (2024). A global database of dissolved organic matter (DOM) concentration measurements in coastal waters (CoastDOM v1). Earth System Science Data, 16(2), 1107-1119.

Section 3.1.1: in addition to the discussion of total carbon fixation, it would be interesting to provide a more detailed analysis of the specific changes in primary production among the three modeled species (flagellates, cyanobacteria, and diatoms).

Summary:

This paper provides a thorough and innovative investigation into the role of variable organic matter (OM) stoichiometry in marine carbon cycling, with a focus on the Northwest European shelf seas (NWES). Variations in the elemental ratios of carbon, nitrogen, and phosphorus are integrated into a high-resolution coupled 3D physical-biogeochemical model to explore their impact on the efficiency of carbon sequestration. The authors demonstrate that variable stoichiometry, implemented through carbon-enriched dissolved OM under nutrient limitation and preferential remineralization of organic nitrogen and phosphorus, enhances net CO₂ uptake by 10–33% in the NWES compared to fixed Redfield stoichiometry. This paper is very well-written, with clear organization and a logical flow that makes its complex subject matter accessible. The figures are well-crafted and effectively support the key findings, reflecting the substantial effort and attention to detail invested in this work. The study represents a significant contribution to marine biogeochemistry by addressing a poorly understood yet critical aspect of carbon cycling. The results are highly relevant to advancing global modeling efforts and improving our understanding of the coastal carbon cycle. I recommend this paper with minor revisions to clarify a few methodological and interpretive details. Below are my specific comments.

A general comment on the terminology used for changes in air-sea CO₂ flux between two model configurations:

In this study, it appears that positive values of air-sea CO₂ flux represent CO₂ uptake by the ocean, while negative values indicate CO₂ outgassing to the atmosphere. The manuscript describes negative changes in air-sea CO₂ flux between two model experiments as “decreased air-sea CO₂ exchange.” However, this phrasing can be ambiguous because air-sea CO₂ exchange can have positive (uptake) or negative (outgassing) values. For example, in cases where CO₂ uptake occurs in the RS experiment (positive flux), a "reduction in air-sea CO₂ exchange" in the ER experiment means less CO₂ uptake, which aligns with the terminology. However, if CO₂ outgassing occurs in the RS experiment (negative flux), a "reduction in air-sea CO₂ exchange" in the ER experiment corresponds to more CO₂ outgassing, which actually implies an increase in exchange.

To avoid confusion, I suggest using more explicit terms, such as:

• "CO₂ uptake is stronger/weaker"
• "CO₂ outgassing is stronger/weaker."

For example, in Line 718, instead of "air-sea CO₂ exchange is decreased by xx%," you could write, "CO₂ uptake by the ocean is reduced by xx%" or "CO₂ outgassing to the atmosphere is enhanced by xx%," depending on the context. Please consider checking this throughout the manuscript. This more straightforward phrasing would improve clarity and ensure that readers can unambiguously interpret the results.

Detailed comments:

1. Line 176: Does this imply that the growth of the other two phytoplankton groups is not temperature-dependent? What might be the downsides of making such assumptions in the model?
2. Line 181: Why are POM and DOM productions partitioned into 60% and 40%? What is the reference for this assumption?
3. Line 225: If I understand correctly, the extracellular release of DOM should balance with the consumption of nutrients and DIC from a mass balance perspective (without altering phytoplankton biomass). Could the authors clarify this in the Methods section? I noticed a description of DIC consumption in line 279 but found no description to nutrient (N and P) consumption.
4. Line 225: Is the scaling factor B_ER ​used as a knob to tune the model? Is this an arbitrary number? Please clarify.

5. Line 242: If this was the case, I would expect E_DON​ to be set to zero when β_N≤0.1 or β_P≥1 in Equation (2). However, maybe it doesn’t matter to include β_P≥1 in Equation (2) because this might not occur in practice.

6. Line 265: What do the “+60%” and “+100%” in Figure 3 represent?

7. Line 294: In the PR experiment, was ER turned off?

8. Line 297: Is there a reason the authors chose a lower ER scaling factor and reduced increase in bioavailability in the ER&PR configuration? As a reader, I’m curious whether the impacts of ER and PR are linearly additive (i.e., using the same settings as in the individual ER and PR configurations). To clarify, I am not requesting the authors to conduct additional model experiments.
9. Line 321: Did the authors use TA at the zero-salinity endmember from TA vs. salinity relationships for those rivers without data?
10. Lines 579-580: These paired numbers refer to rates in the North Sea and the entire NWES. However, on first reading, it seems as if 4.5 and 9.6 refer to pelagic remineralization and carbon fixation, respectively. Could the authors rephrase to avoid this confusion?
11. Line 690: I don’t necessarily agree with the statement of “decrease in the air-sea CO2-exchange” here. The blue coloration along the Norwegian coast and other regions seems to indicate that outgassing is stronger due to ER, rather than a decrease in air-sea CO₂ exchange. It appears that the effect of ER is to enhance CO₂ uptake in previously uptake-dominant regions and increase CO₂ outgassing in outgassing-dominant regions. While this may not hold everywhere, it seems to be the general pattern in Figure 11. Please correct me if I have misunderstood.

12. Lines 718, 720: Please see my general comment #1. Additionally, the authors may want to check for similar phrasing elsewhere in the manuscript.

13. Line 735: I believe Figure S18 shows the spatial distribution of air-sea CO₂ exchange for four experiments, rather than the differences between two experiments, as suggested by the titles of the last three columns.

14. Line 789: Why did the authors choose vertically integrated DIC to describe differences between model experiments? I appreciate this approach, but is it because changes in concentrations are small in Figure 14? It’s somewhat hard to discern differences in the vertical profiles. Perhaps there are better ways to present the changes in the DIC profile? Just a thought to consider.