the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Oct 2025
Marine particles and their remineralization buffer future ocean biogeochemistry response to climate warming
Abstract. Transport and fate of particulate organic carbon (POC) and nutrients through marine particles co-determine the future response of ocean biogeochemistry and oceanic carbon uptake under climate warming. This makes the parametrization of the biological carbon pump in Earth system models (ESMs) an important model component and motivates us to compare a recently developed new sinking scheme (M4AGO; Maerz et al. 2020) to the current CMIP6 default Martin curve-like sinking scheme in MPI-ESM1.2-LR (see Mauritsen et al. 2019) under the future shared socio-economic pathway high-emission scenario SSP5-8.5. In their global response, the two model versions are similar, showing a decrease of integrated net primary production between the historical (1985–2014) and future (2070–2099) period of about 8.1 % and 9.7 % for the CMIP6 and M4AGO version, respectively. However, the models response differs latitudinally. In M4AGO, the temperature-dependent remineralization offsets the future increase in sinking velocity caused by a higher CaCO3 to POC ratio in the low latitudes. There, M4AGO thus buffers the export loss of nutrients to the mesopelagic, visible in little future changes of the export to net primary production ratio (the p ratio), while the CMIP6 version shows more pronounced changes with regionally declining or increasing p ratio. In the Arctic Ocean, the projected future increase of net primary production in the CMIP6 version is diminished with M4AGO through its higher POC transfer efficiency in high latitude regions. Hence, the more mechanistic and to environmental changes-responding M4AGO scheme shows a stronger buffering regional response to climate warming than the CMIP6 model version. The higher transfer efficiency also impinges on higher CO2 uptake in high latitude regions while the tropical regions turn later into a net sink with M4AGO compared to the standard CMIP6 version. Next to ballasting, we identified the particle microstructure as vigorous determinant for future changes of POC sinking velocity. Microstructure co-determines particle porosity and particle density. Processes governing the microstructure thus can be regarded as decisive to understand for reducing uncertainty of future POC fluxes.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4815', Anonymous Referee #1, 25 Nov 2025
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RC2: 'Comment on egusphere-2025-4815', Anonymous Referee #2, 02 Dec 2025
Review of “Marine particles and their remineralization buffer future ocean biogeochemistry response to climate warming” by Maerz et al.
The manuscript by Maerz et al. provides an important synthesis of information related to particle formation, sinking, and remineralization processes in ocean biogeochemical models. The authors systematically compare a simple model version -referred to as “CMIP6”- with one that includes more complex particle-sinking and remineralization processes, “M4AGO”, for both historical and future periods. They offer a detailed analysis of projected changes and assess the impact of using a more complex representation of particle sinking and remineralization.
The authors document the effect of representing marine particles and their remineralization in a more complex way (e.g., temperature- and oxygen-dependent remineralization, the effect of particle microstructure on sinking speed, such as the influence of ballast minerals) on climate projections. They clearly show that two regions most affected in terms of transfer efficiency are oxygen-deficient zones and the Arctic Ocean. These findings are highlighted in the results from the more complex model, “M4AGO” simulations. Authors also compare future changes (2070–2099) with historical periods (1985–2014) using two model versions: the simpler ‘CMIP6’ and the more complex ‘M4AGO.’ They demonstrate that marine particles play a role in buffering the future ocean biogeochemistry response to climate warming, especially in the tropical and subtropical regions. They detail how a simple representation of marine particles in a climate model could alter projections of future p-ratios in these regions.
Overall, the manuscript is well written, well organized, and highly informative for the biogeochemical modeling community. It makes a significant contribution to ongoing research on the biological carbon pump using Earth system models. However, given the frequent references to Maerz et al. (2020) and Mauritsen et al. (2019) and the length of the manuscript, the Methods/Conclusion sections could benefit from adjustments to clarify the comparisons for readers.
My specific suggestions and comments are listed below:
Line 117: I suggest adding a small table or a simple illustration highlighting the differences between the CMIP6 version and M4AGO. As it is, the reader needs to refer back to Mauritsen et al. (2019) and Maerz et al. (2020) to fully understand the setup. A summary of the key differences would make the comparison easier to follow in the subsequent sections. A similar addition could be made for Section 2.2.
Line 185: These kinds of metrics are difficult to standardize. In the biogeochemical modeling community, different metrics are used for similar analyses, but they represent different concepts, such as the f-ratio, e-ratio, p-ratio, and s-ratio. In this manuscript, the p-ratio is chosen to represent export efficiency, defined as the ratio of export flux to NPP. Since you frequently cite Laufkötter et al. (2016), it might be less confusing for readers referencing the same literature if you adopt consistent notation and explicitly cite that paper when introducing the metric. In Laufkötter et al. (2016), the p-ratio refers to the ratio of total POC to NPP, while the e-ratio is defined as export efficiency, the ratio of export flux to NPP. I am aware that p-ratio is also used in Maerz et al. (2020); I just wanted to raise this point for clarity, in case authors wish to change.
Line 204: ‘Higher remineralization’:
I noticed that the comparison of remineralization rates between the two models is not shown in any of the figures presented in the manuscript. Could this be added as a supplementary figure? Adding a figure would help confirm whether the observed differences are indeed due to higher remineralization.
When I checked the sinking speed from the standard model, it appears to be 3.5 m d⁻¹ at the top 100 m. In contrast, in M4AGO, the concentration-weighted mean sinking velocity seems to be higher in the subtropics (Figure D1). Typically, one would expect higher nutrient export from the euphotic zone in a shorter time under such conditions. However, as stated, remineralization in the M4AGO case is significantly higher. Could you clarify how this balance between sinking speed and remineralization impacts nutrient export in models?
Regarding your decision to adopt temperature-dependent remineralization with Q10 factors, what was the motivation behind this choice? Would you expect that the results would change a lot depending on your Q10 choice?
Line 208: The statement about "increasing stratification, weaker mixing, and less recovery of exported nutrients" is compelling. However, it would be helpful to back this up with evidence showing the relationship between increased temperature or increased stratification in your model results. Including figures in the appendix would strengthen the argument and make the reasoning easier to follow.
Line 251: When I read Equation 2, the transfer efficiency appears to be independent of NPP. The primary driver of its change is the balance between sinking speed and remineralization. The manuscript states that adding POM increases the buoyancy of marine particles, thereby decreasing sinking velocity. Would it be more effective to integrate this explanation with the discussion on changes in particle properties in Section 3.4? While Appendix C also conveys this message, readers must carefully analyze the notations and navigate a rather crowded figure to understand it fully. Simplifying or consolidating these points in the main text could improve clarity.
Line 260: Can you clarify how a reader can see that the Weddell and Ross seas from Figure 4c?
Line 505-560: The manuscript could benefit from a summary figure that highlights all key changes documented across the results sections (e.g., responses of Arctic, subtropical, and tropical regions). Adding such a figure would be beneficial, given the large amount of information presented in the paper, as it could help a broader audience beyond just biogeochemical modelers. A concise visual summary would make it easier for readers to understand and engage with the key findings.
Typos:
Figure 4 caption: Typo "standrad” - should be "standard".
Line 497: Typo in "mesopelgic" - should be "mesopelagic".
Citation: https://doi.org/10.5194/egusphere-2025-4815-RC2
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The study by Maerz et al. provide an extensive analysis of the M4AGO parametrisation in a context of climate change. This parametrization includes temperature-dependant remineralization, oxygen limitation of remineralization, sea water viscosity, ballasting (composition) and a microstructure (fractal dimension / porosity) representation with aggregation/desagregation processes including particle density, size and stickiness. Sinking velocity is ultimately considering sea water viscositiy, particle composition (of ballast material), particle density, porosity and size. The study is very well written (although very verbose and some times convoluted), and provide a dense, comprehensive, well referenced, honest (especially about the limited impacts on global air-sea CO2 fluxes and limitations in general) and transparent analysis of this ambitious parametrization. The authors demonstrate a very high level of mastery in their disciplines.
They found little influence and global scale but highlighted regional important differences such as in the Arctic Ocean.
The review of this article was challenging. About 23 pages that must also include the lengthy study of Maerz et al. 2020 (another very lengthy and technical paper introducing the M4AGO parametrization). The writing is sometimes lengthy and technical. The paper in general would deserve a more synthetic and accessible bite. The problem with that is I really wonder who is able to read and actually digest this article beyond the small BGC modeling community.
The other problem is related to the microstructure parametrization which the authors claim is an important factor elucidating regional patterns of the BCP. If all other parametrizations are relatively simple and are developed similarly in other models, the microstructure parametrization increase complexity substantially with a lot of under- (or non-) -constrained parameters (see Maerz et al. 2020). I am aware that the authors already acknowledge this, guaranty computational efficiency and provide quantitative effects. Still, this is very hard to proof what is done here especially noting that the code is not open. How can this parametrization could be evaluated with observations? (The comparison with CO2 fluxes does not necessarily show an improvement to be honest). How could we constrain more the numerous parameters? (although if not achievable now, what could be used in the future?) I know they acknowledge there is little information available so far, but how could we proceed then? Are we sure the regional patterns are more realistic?
On the regional aspect, the authors put a lot of emphasis on the Arctic Ocean (and OMZ). If conclusions rather make sense for most regions to me, I was still puzzled by the conclusions drawn for the Arctic mostly because of the lack of synthesis capacities of the authors. Explanations are scattered around which makes a lot of work for the reader to re-assemble the results and conclusions. They show that M4AGO allows higher transfer efficiency compared with CMIP6 (Appendix D show maximum sinking velocity in the Arctic? Why?). But climate induce change towards:
=> If I understand well, this overall has the effect of decreasing the sinking velocity in the Arctic (Appendix D)
However, this is combined with:
I finally got the sense of the overall message: The total effect is RLS & transfer efficiency decrease despite increase in NPP (positive feedback loop). The authors should wrap this up somewhere better, it’s not an intuitive result. Same for other regions eventually.
This article is certainly worth publishing, but I would recommend a few changes and clearer explanations before doing so.
I have noted point-by-point comments below:
Line 107: What are the limitations of such hypothesis ? In general this does not stand in case of strong lateral advection.
Section 2.2: Why not adjusting calcite ?
Line 161: physical internal variability is not assessed, is there any differences in the physical fields ?
Line 165: Not true. Stratification only decrease in the Atlantic sector of the Arctic. Fix also statement line 227.
Line 174: With all due respect, this sentence is too complicated. There is sea ice now and there always will be … in winter. You are talking about summer sea ice. Seasonality, I guess you refer to the winter polar night (absence of light -> no NPP). And yes the Arctic is a small ocean but what the point if you discuss relative changes in % ?
Line 176: 100m is not the euphotic depth. It is a simplified threshold depth considered as the euphotic depth. Of course, much less accurate that an actual calculation of the euphotic depth (variable in time and space) to derive the export production. It’s fine ! But reformulate.
Line 179: While still using the SSP585 while we know this is not the way to go ?
Hausfather, Z. & Peters, G. P. Emissions – the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).
Line 204: Did I miss the obvious or the remineralization is not shown ?
Line 233: Sequestration. I have also used this word wrongly for while, I am not blaming, but could we fix that? You can refer to the nice Visser 2025 which clarifies:
“carbon sequestration is synonymous with an offset of carbon emissions”
https://doi.org/10.1002/lol2.70053
replace by storage at greater depth or similar.
Line 255: Arctic Ocean amplification, ref:
Shu, Q. et al. Arctic Ocean amplification in a warming climate in CMIP6 models. Sci. Adv. 8, eabn9755 (2022).
I agree but this is counter-intuitive for most reader and non-experts. Can you clarify here quickly what is meant ? You mean that there is more POM and therefore, relatively, less ballast material in the composition of particles if I refer to Appendix C. Why seasonal average ?
Line 260: If the inter-annual variability is represented by the STD, say it.
Line 270-272: needed ?
Line 274: Even a flux cannot ! Only change in storage.. See article by Frenger, I. et al. Misconceptions of the marine biological carbon pump in a changing climate: thinking outside the ‘export’ box. Glob. Change Biol. 30, e17124 (2024).
Line 309: you mean vertical DIC gradient right ? fix through the text.
Line 316: I can understand why (simulations from data product or your simulation) internal variability is a problem, but why the mean of the observational product is ?
Line 355: time-cumulative ?? you mean yearly integrated ?
Line 365: It is appreciated that the authors acknowledge that physico-chemical process dominate air-sea CO2 fluxes dynamics. Although this is repeated several times in the manuscript.
Line 370: Yes the BCP if responsible for the most part of the vertical DIC gradient. Rephrase.
Line 410: I don’t understand how more detritus production necessarily leads to less compact & bigger particles.
Line 420 : Explain me how temperature dependant remineralization has a direct effect on particles density and porosity ? You mean temperature in general ? I don’t understand this sentence.
Line 338: variable distribution slope ? you mean the size distribution? Not clear to me.
Line 500: Likely true. Positive feedback loop maybe see Oziel et al. 2025. Not represented in CMIP6 models… not so sure, prove it.
Line 516: between
Line 545: “more realistic” in terms of process maybe, but in terms of model performance ? Not sure.