the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Nov 2024
SISSOMA (v1): modelling marine aggregate dynamics from production to export
Abstract. A mechanistic approach linking the population dynamics of plankton communities to the export of detrital material to the oceans interior, remains a largely unresolved component of global bio-geochemical models. We propose that the self-similarity of aggregation provides a tractable modelling framework for simulating the dynamics and sinking speed of natural marine particle aggregates. It provides a means to track both size and excess density of aggregates as they are formed and transformed by aggregation, degradation and fragmentation processes. A self-similarity parameter a in the range 1.8 to 2.1 is well supported by direct observations drawn from an extensive database of aggregate size and sinking speed. We provide a simple model, SISSOMA, that uses a 2 dimensional state-space representation of aggregate dynamics for which we conduct sensitivity analyses for the self-similarity parameter, stickiness, turbulent dissipation rate and the production rate of primary particles. The model provides size and density resolved estimates of the export flux of detrital material generated by a diverse community of primary producers. While open to improvement in several aspects, the model compares well with observations of aggregate size spectra covering the global ocean.
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RC1: 'Comment on egusphere-2024-2520', Anonymous Referee #1, 14 Dec 2024
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The manuscript by Visser et al. introduces the modeling framework “Sissoma,” which offers a valuable approach to better estimate the excess density and sinking velocity of marine aggregates by considering fragmentation and aggregation processes. A noteworthy strength of the framework is its ability to incorporate various primary particle properties, enabling the model outcomes (e.g., aggregate numbers, sizes, and excess density) to be linked directly to specific primary producers. This is a promising contribution to the field.
The manuscript is technically robust and well-written, making it a pleasure to read. However, in most sections, the authors appear to overstate the implications of their model results by using absolute statements that are not fully supported by the presented evidence (see the first major comment below). There are also many statements made that need references throughout the manuscript. Additionally, the discussion would benefit from greater integration with existing literature, particularly through comparisons with other modeling approaches that have explored similar approaches, including those used in Earth System Models (e.g., doi.org/10.5194/bg-17-1765-2020 ).
If the authors prefer to retain these strong claims, I would recommend providing a more detailed comparison between the model results and actual measurements. So far, the authors are only comparing ranges. Comparisons could be achieved, for instance, by leveraging databases such as Ecopart to start with comparisons of aggregate properties. That said, I believe the manuscript would stand out even more by adopting a more measured tone, which would make its conclusions appear even more credible and impactful.
Major comments:
(1) The authors should address the absolute statements made throughout the manuscript. For instance, the claim that “the model compares well with observations of aggregate size spectra covering the global ocean” is not accurate. The paper does not present direct comparisons between modeling results and measurements; only ranges are compared. Similarly, the statement that “a mechanistic approach linking the population dynamics of plankton communities to the export of detrital material to the ocean’s interior remains a largely unresolved component of global biogeochemical models” is broadly true but somewhat misleading in this context. The manuscript does not provide this mechanistic link. Upon first reading, this statement led me to expect a population dynamics model coupled with fractal theory, which is not the case. Similar statements are made throughout the manuscript and should be adjusted accordingly.
(2) The manuscript is not the first to apply fractal theory to the aggregation and fragmentation of marine aggregates. The authors should more clearly articulate the novelty of their approach and specify where similar methods have been employed in the existing literature (particularly in the introduction and discussion). Within the manuscript mostly the studies that focus on the basic theories that are covered.
(3) The ocean features a stratified water column which influences the excess density of aggregates. Numerous studies from various laboratories have demonstrated these effects, particularly around pycnoclines (e.g. below mixed layer depth), as well as in the linear stratification commonly observed throughout the ocean. While I don’t believe it is necessary to incorporate these aspects into the model at this stage, a discussion of this limitation would improve the manuscript.
(4) The authors focus on the excess density of aggregates which integrates the solid hydrated density and porosity of aggregates and is a typical approach taken. However, measurements of the last decades indicate that these two parameters (porosity+solid hydrated density) have their own specific composition-dependent variability that is difficult to estimate through the holistic approach taken. These limitations should be addressed in more detail, so far the porosity is only measured briefly in the discussion and solid-hydrated density is not covered at all.
(5) As a recommendation: Throughout the text, the authors mention properties that are difficult to measure. As a researcher with a focus on experimental approaches to studying marine aggregates, I believe it would be a valuable addition to the manuscript to highlight which parameters should be measured in more detail in future.
Line specific comments:
Line 120: A reference is missing that shows that the particle size spectra produced from roller tanks are not representative of natural aggregate communities. Also: I find the term “natural aggregate communities” confusing and would just refer to natural aggregates.
Line 119: Throughout the manuscript, the authors occasionally use "particles" to refer to aggregates and at other times to primary particles. While this usage is not necessarily incorrect (depending on the definition), it can be confusing for the reader. I recommend clearly distinguishing the terminology between primary particles and aggregates to improve clarity.
Equation 8: A short name for the individual terms next to the equation would make it easier for the reader to following the different processes.
Figures
In my opinion, the authors could make the paper more accessible to a broader readership by simplifying the figures. The axes should directly represent the variables being displayed, and the captions should be concise and clear, avoiding unnecessary complexity. Figure 1, in particular, appears somewhat random, as it simply demonstrates that Stokes’ law with a constant excess density does not predict sinking velocity accurately—an observation already established in the literature. I would expect this figure to show an improvement in the settling-velocity prediction, which is currently missing.
Comment on the code provided
I have tested the code provided by the authors in Matlab Mathworks 2024a and the main function is operational, but the batch files are not (e.g. output file expA.mat is not existing) and the batchRun misses the parameter definition. Overall, the code is well documented which I appreciate.
Citation: https://doi.org/10.5194/egusphere-2024-2520-RC1
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