the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Mar 2025
Improving Marine Sediment Carbon Stock Estimates: The Role of Dry Bulk Density and Predictor Adjustments
Abstract. Continental shelves are critical for the global carbon cycle, storing substantial amounts of organic carbon (OC) over geological timescales. Shelf sediments can also be subject to considerable anthropogenic pressures, offshore construction and bottom trawling for example, potentially releasing OC that has been sequestered into sediments. As a result, these sediments have attracted attention from policy makers regarding how their management can be leveraged to meet national emissions reductions targets. Spatial models offer solutions to identifying organic carbon storage hotspots; however, data gaps can reduce their utility for practical management decisions. Regional spatial models of OC often use global scale predictors which may have biases on regional scales. Moreover, dry bulk density (DBD), an important factor in calculating OC stock from sediment OC content, has comparatively few data points globally. We compared two spatial models of OC stock in the Irish Sea, one using unadjusted predictors and a previously used method to estimate DBD, and another incorporating bias-adjusted predictors, from in situ data, and a machine learning-based DBD model, to assess their relative performance. The adjusted model predicted a total OC reservoir of 46.6 ± 43.6 Tg within the Irish Sea, which was 31.4 % lower compared to unadjusted estimates. 70.1 % of the difference between adjusted and unadjusted OC stock estimates was due to the approach for estimating DBD. These findings suggest that previous models may have overestimated OC reservoirs and emphasizes the influence of accurate DBD and predictor adjustments on stock estimates. These findings highlight the need for increased in situ DBD measurements and refined modelling approaches to enhance the reliability of OC stock predictions for policy makers. This study provides a framework for refining spatial models and underscores the importance of addressing uncertainties in key parameters to better understand and manage the carbon sequestration potential of marine sediments.
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Status: closed
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RC1: 'Comment on egusphere-2025-661', Anonymous Referee #1, 24 May 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-661/egusphere-2025-661-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-661-RC1 -
AC1: 'Reply on RC1', Mark Chatting, 30 Jun 2025
We would like to thank the reviewer for their thorough and constructive feedback. Please find attached our responses to RC1. Below we have provided RC1’s comments (in black text) and our responses in red italic text. Where RC1 gave comments in paragraph form, we underlined key concerns in the paragraph and pasted them below with out associated response to ensure we addressed all of RC1’s comments. Other than that we have addressed each one of RC1’s comments in red italic text after each specific comment.
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AC1: 'Reply on RC1', Mark Chatting, 30 Jun 2025
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RC2: 'Comment on egusphere-2025-661', Anonymous Referee #2, 11 Jun 2025
Review of M. Chatting et al. : “Improving Marine Sediment Carbon Stock Estimates: The Role of Dry Bulk Density and Predictor Adjustments”, egusphere-2025-661
Summary
The present manuscript by Chatting et al. describes an improved modelling approach for calculating OC stock for marine shelf areas by upscaling point observations on OC content. Two approaches are compared: (1) The traditional one uses available globally resolved datasets for predictor variables, including dry bulk density as calculated from sediment porosity and combines it with point observations of OC content in the Irish Sea to derive the local OC stock using a random forest approach. (2) The improved one first performs a bias adjustment, which transforms global data to better represent local point observations. Dry bulk density is then extrapolated using a random forest model based on these transformed predictor data. A second random forest model is applied for the OC content and lastly dry bulk density and OC content are combined into a new OC stock estimate. The new approach shows improved performance (i.e. agreement with in situ data) in predicting dry bulk density and lowers the OC stock estimate for the region by around one third.
General Comments
The approach is methodologically sound. Bias adjustment has so far been mostly applied in climate models and incorporating it into marine OC stock estimation is a novel but timely application. The other modelling approach, random forest models, is well established in geospatial modelling and is an appropriate choice for the present case. The manuscript convincingly shows that bias adjustment as well as including refined DBD estimates can substantially improve our OC stock assessments.
However, the MS is in some section hard to follow, especially in the methods section. A more clear description of the modelling workflow (supported by Fig 1), a plain language summary of what bias adjustment (a central method of this study) entails, and how the success of these measures is actually measured should appear in the introduction or early in the methods sections. This way the reader can be more effectively guided through the novel data handling approach.
Another point of concern which needs to be clarified is the choice of study area. It does not become clear what the advantages of choosing the Irish Sea are, although certainly there were some. The authors mention that only 3% of DBD data (the most important predictor variable!) are available for the study area. Either expanding the scope of the modelling to the entire NW European shelf, which is the source for this DBD data, or making a good case for limiting it to the Irish Sea are needed.
Despite these points the present MS provides a useful and novel blueprint study which can help improving OC stock modelling globally, a topic with general relevance for climate science as well as biogeochemistry.
Specific Comments
- Clarify methods: The manuscript would benefit from some more plain language step-by-step guidance throughout the methods section. E.g. in L66-72 or later, a comprehensible description of what QQ mapping entails could help familiarizing the reader with the approach.
- Choice of study area: The authors should better justify their focus on the Irish Sea (e.g. in L22-23), especially as DBD seems to be sparsely available here. The reader gets the impression the entire shelf area might have been a better focus (600+ data points for DBD). Certainly there are arguments for this tighter spatial focus, which could be presented here.
- Generalizability: The authors should briefly state how the findings are transferable to other geographical regions, and what it would mean for existing OC stock assessments. Is this only applicable in areas with dense available data and therefore limited to well studied zones, or can we improve global estimates? How, e.g., would the results of Atwood et al. (2020) which are cited in the text change considering the findings of the MS? Adding a global relevance section to the discussion can help the reader better grasp the implications of the novel modeling framework.
Line comments
L15: the study addresses the short-term C cycle, not the geological one, consider rephrasing
L19: “Data gaps” may be misleading here. The study does not collect new data but rather improves the interpretation of existing data, rephrase
L22-23: State why Irish Sea was chosen; maybe due to sufficient data availability?
L28: “emphasize” instead of “emphasizes” as it refers to “findings”
L29: ensure consistent formatting of in situ (throughout the MS)
L30: consider removing “for policy makers”, as many more stakeholders are interested in improved OC stock assessment
L49-50: harmonize phrasing “uppermost 10 cm”, “surficial 0-10 cm” and “top 0.1 m” (in methods section) all refer to the same and should be consistent
L56: possibly use “OC stock” instead of “OC content”; the sentence is hard to read
L58: “sediment density” should be “soil density”
L59: consider removing “however” to improve flow
L66-72: The repeated mention of “climate models” and references about them can be drastically shortened. Instead it could include a one sentence, plain-language summary of what bias adjustment entails
L78: If OC and mud content measurements also use similar instrumentation maybe mention this (instead)
L87: Be more clear how model improvement is assessed
L97: Does this mosaic of sediment types help modelling here?
L104-105: Briefly mention how inshore area is defined in this data
L147: Add a sentence as plain-language summary of QQ mapping
L153-165: Consider moving to Supplementary, log ratio transformation is a standard procedure in compositional data and not crucial to the presented modelling approach
L192: Is this the mud content from spatial averaging? Clarify
L206: refer to Fig 4 here (may be Fig 3 then)
L214: could this k fold CV be replaced with the NNDM LOO CV, which is introduced later and said to perform better?
L236: “by a grid cell” instead of “by grid cell”
L241: be more explicit than “all possible combinations”, there are 4 combinations; OC adj/unadj with DBD adj/unadj correct?
L273: Sort plots by predictor importance in Figure 4
L277: How would this approach perform in regions with even fewer DBD observations?
L318-320: Clarify how the presented findings would influence their estimates
L326: move the definition of mud to its first mention in the MS
L331-335: The sentence is long and unclear; what is the “topography” of a mineral grain? Also the references need sorting
L346: Space missing between “(2024)” and “estimated”
L356: “resuspension” instead of “suspension”
L362-63: “needs” instead of “need”
L388: “carries” instead of “carry”
L398: rephrase “increased in situ data” to “increased availability of in situ data”
Figure 2: It seems the yellow shade is not reached/used, maybe adjust the colormap. Also the thick outline is not very visually appealing and might obscure data points. A dashed line should be tested.
Figure 4: The plot is not very visually appealing, it is not clear what the letters refer to. Maybe the distance between the a and b row can be slightly increases. Plots should be sorted in the order of relative parameter importance. Also the figure caption for 4 c is not correctly describing the presented plots.
Figure 5, 6 and 7 all show the same region, but different parameters. Maybe combining them all into one large, page filling figure would allow the reader to better appreciate all present trends at once. Another side note is, that the text mentions the “Isle of Man”, which is not labelled in a map and might be unfamiliar to many readers.
Citation: https://doi.org/10.5194/egusphere-2025-661-RC2 -
AC2: 'Reply on RC2', Mark Chatting, 30 Jun 2025
We thank RC2 for their constructive and thorough feedback. Please find attached our responses to RC2's comments in red italic text. Where RC2 answered in paragraph form we pasted specific queries below the paragraph and responded in red italic text.
-
AC3: 'Reply on RC2', Mark Chatting, 30 Jun 2025
Please find attached out responses to RC2's comments. We have provided our responses in red italic text. When the reviewer gave feedback in paragraph form, we cut and paste specific queries below the paragraph and gave our responses in red italic text.
Status: closed
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RC1: 'Comment on egusphere-2025-661', Anonymous Referee #1, 24 May 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-661/egusphere-2025-661-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Mark Chatting, 30 Jun 2025
We would like to thank the reviewer for their thorough and constructive feedback. Please find attached our responses to RC1. Below we have provided RC1’s comments (in black text) and our responses in red italic text. Where RC1 gave comments in paragraph form, we underlined key concerns in the paragraph and pasted them below with out associated response to ensure we addressed all of RC1’s comments. Other than that we have addressed each one of RC1’s comments in red italic text after each specific comment.
-
AC1: 'Reply on RC1', Mark Chatting, 30 Jun 2025
-
RC2: 'Comment on egusphere-2025-661', Anonymous Referee #2, 11 Jun 2025
Review of M. Chatting et al. : “Improving Marine Sediment Carbon Stock Estimates: The Role of Dry Bulk Density and Predictor Adjustments”, egusphere-2025-661
Summary
The present manuscript by Chatting et al. describes an improved modelling approach for calculating OC stock for marine shelf areas by upscaling point observations on OC content. Two approaches are compared: (1) The traditional one uses available globally resolved datasets for predictor variables, including dry bulk density as calculated from sediment porosity and combines it with point observations of OC content in the Irish Sea to derive the local OC stock using a random forest approach. (2) The improved one first performs a bias adjustment, which transforms global data to better represent local point observations. Dry bulk density is then extrapolated using a random forest model based on these transformed predictor data. A second random forest model is applied for the OC content and lastly dry bulk density and OC content are combined into a new OC stock estimate. The new approach shows improved performance (i.e. agreement with in situ data) in predicting dry bulk density and lowers the OC stock estimate for the region by around one third.
General Comments
The approach is methodologically sound. Bias adjustment has so far been mostly applied in climate models and incorporating it into marine OC stock estimation is a novel but timely application. The other modelling approach, random forest models, is well established in geospatial modelling and is an appropriate choice for the present case. The manuscript convincingly shows that bias adjustment as well as including refined DBD estimates can substantially improve our OC stock assessments.
However, the MS is in some section hard to follow, especially in the methods section. A more clear description of the modelling workflow (supported by Fig 1), a plain language summary of what bias adjustment (a central method of this study) entails, and how the success of these measures is actually measured should appear in the introduction or early in the methods sections. This way the reader can be more effectively guided through the novel data handling approach.
Another point of concern which needs to be clarified is the choice of study area. It does not become clear what the advantages of choosing the Irish Sea are, although certainly there were some. The authors mention that only 3% of DBD data (the most important predictor variable!) are available for the study area. Either expanding the scope of the modelling to the entire NW European shelf, which is the source for this DBD data, or making a good case for limiting it to the Irish Sea are needed.
Despite these points the present MS provides a useful and novel blueprint study which can help improving OC stock modelling globally, a topic with general relevance for climate science as well as biogeochemistry.
Specific Comments
- Clarify methods: The manuscript would benefit from some more plain language step-by-step guidance throughout the methods section. E.g. in L66-72 or later, a comprehensible description of what QQ mapping entails could help familiarizing the reader with the approach.
- Choice of study area: The authors should better justify their focus on the Irish Sea (e.g. in L22-23), especially as DBD seems to be sparsely available here. The reader gets the impression the entire shelf area might have been a better focus (600+ data points for DBD). Certainly there are arguments for this tighter spatial focus, which could be presented here.
- Generalizability: The authors should briefly state how the findings are transferable to other geographical regions, and what it would mean for existing OC stock assessments. Is this only applicable in areas with dense available data and therefore limited to well studied zones, or can we improve global estimates? How, e.g., would the results of Atwood et al. (2020) which are cited in the text change considering the findings of the MS? Adding a global relevance section to the discussion can help the reader better grasp the implications of the novel modeling framework.
Line comments
L15: the study addresses the short-term C cycle, not the geological one, consider rephrasing
L19: “Data gaps” may be misleading here. The study does not collect new data but rather improves the interpretation of existing data, rephrase
L22-23: State why Irish Sea was chosen; maybe due to sufficient data availability?
L28: “emphasize” instead of “emphasizes” as it refers to “findings”
L29: ensure consistent formatting of in situ (throughout the MS)
L30: consider removing “for policy makers”, as many more stakeholders are interested in improved OC stock assessment
L49-50: harmonize phrasing “uppermost 10 cm”, “surficial 0-10 cm” and “top 0.1 m” (in methods section) all refer to the same and should be consistent
L56: possibly use “OC stock” instead of “OC content”; the sentence is hard to read
L58: “sediment density” should be “soil density”
L59: consider removing “however” to improve flow
L66-72: The repeated mention of “climate models” and references about them can be drastically shortened. Instead it could include a one sentence, plain-language summary of what bias adjustment entails
L78: If OC and mud content measurements also use similar instrumentation maybe mention this (instead)
L87: Be more clear how model improvement is assessed
L97: Does this mosaic of sediment types help modelling here?
L104-105: Briefly mention how inshore area is defined in this data
L147: Add a sentence as plain-language summary of QQ mapping
L153-165: Consider moving to Supplementary, log ratio transformation is a standard procedure in compositional data and not crucial to the presented modelling approach
L192: Is this the mud content from spatial averaging? Clarify
L206: refer to Fig 4 here (may be Fig 3 then)
L214: could this k fold CV be replaced with the NNDM LOO CV, which is introduced later and said to perform better?
L236: “by a grid cell” instead of “by grid cell”
L241: be more explicit than “all possible combinations”, there are 4 combinations; OC adj/unadj with DBD adj/unadj correct?
L273: Sort plots by predictor importance in Figure 4
L277: How would this approach perform in regions with even fewer DBD observations?
L318-320: Clarify how the presented findings would influence their estimates
L326: move the definition of mud to its first mention in the MS
L331-335: The sentence is long and unclear; what is the “topography” of a mineral grain? Also the references need sorting
L346: Space missing between “(2024)” and “estimated”
L356: “resuspension” instead of “suspension”
L362-63: “needs” instead of “need”
L388: “carries” instead of “carry”
L398: rephrase “increased in situ data” to “increased availability of in situ data”
Figure 2: It seems the yellow shade is not reached/used, maybe adjust the colormap. Also the thick outline is not very visually appealing and might obscure data points. A dashed line should be tested.
Figure 4: The plot is not very visually appealing, it is not clear what the letters refer to. Maybe the distance between the a and b row can be slightly increases. Plots should be sorted in the order of relative parameter importance. Also the figure caption for 4 c is not correctly describing the presented plots.
Figure 5, 6 and 7 all show the same region, but different parameters. Maybe combining them all into one large, page filling figure would allow the reader to better appreciate all present trends at once. Another side note is, that the text mentions the “Isle of Man”, which is not labelled in a map and might be unfamiliar to many readers.
Citation: https://doi.org/10.5194/egusphere-2025-661-RC2 -
AC2: 'Reply on RC2', Mark Chatting, 30 Jun 2025
We thank RC2 for their constructive and thorough feedback. Please find attached our responses to RC2's comments in red italic text. Where RC2 answered in paragraph form we pasted specific queries below the paragraph and responded in red italic text.
-
AC3: 'Reply on RC2', Mark Chatting, 30 Jun 2025
Please find attached out responses to RC2's comments. We have provided our responses in red italic text. When the reviewer gave feedback in paragraph form, we cut and paste specific queries below the paragraph and gave our responses in red italic text.
Data sets
Developing Bias-Adjusted Predictors and Machine Learning Models for Organic Carbon Stock Estimation in the Irish Sea Mark Chatting https://doi.org/10.5281/zenodo.14859982
Model code and software
Bias-Adjusted-OC-Stock-Model Mark Chatting https://github.com/markchatting/Bias-Adjusted-OC-Stock-Model.git
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