Submitted as: development and technical paper 15 Jan 2021
Submitted as: development and technical paper | 15 Jan 2021
A discrete interaction numerical model for coagulation and fragmentation of marine detritic particulate matter
- 1Institut des sciences de la mer de Rimouski, UQAR, Québec-Océan, Rimouski (Quebec), Canada
- 2Instituto Antártico Argentino, Buenos Aires, Argentina
- 3Centro Austral de Investigaciones Científicas (CADIC-CONICET), Ushuaia, Argentina
- 4Universidad Nacional de Tierra del Fuego, Ushuaia, Argentina
- 5ArcticNet, Québec-Océan, Université Laval,(Quebec), Canada
- 1Institut des sciences de la mer de Rimouski, UQAR, Québec-Océan, Rimouski (Quebec), Canada
- 2Instituto Antártico Argentino, Buenos Aires, Argentina
- 3Centro Austral de Investigaciones Científicas (CADIC-CONICET), Ushuaia, Argentina
- 4Universidad Nacional de Tierra del Fuego, Ushuaia, Argentina
- 5ArcticNet, Québec-Océan, Université Laval,(Quebec), Canada
Abstract. A simplified model, representing the dynamics of marine organic particles in a given size range experiencing coagulation and fragmentation reactions is developed. The framework is based on a discrete size spectrum on which reactions act to exchange properties between different particle sizes. The reactions are prescribed according to triplets interactions. Coagulation combines two particle sizes to yield a third one, while fragmentation breaks a given particle size into two (i.e. the inverse of the coagulation reaction). The complete set of reactions is given by all the permutations of two particle sizes associated with a third one. Since, by design, some reactions yield particle sizes that are outside the resolved size range of the spectrum, a closure is developed to take into account this unresolved range and satisfy global constraints such as mass conservation. In order to minimize the number of tracers required to apply this model to an Ocean General Circulation Model focus is placed on the robustness of the model to the particle size resolution. Thus, numerical experiments were designed to study the dependence of the results on i) the number of particle size bins used to discretize a given size range (i.e. the resolution) and ii) the type of discretization (i.e. linear vs nonlinear). The results demonstrate that in a linearly size discretized configuration, the model is independent of the resolution. However, important biases are observed in a nonlinear discretization. A first attempt to mitigate the effect of nonlinearity of the size spectrum is then presented and shows significant improvement in reducing the observed biases.
Gwenaëlle Gremion et al.
Status: open (until 18 Mar 2021)
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CEC1: 'Comment on gmd-2020-423', Astrid Kerkweg, 26 Feb 2021
reply
Dear authors,
in my role as Executive editor of GMD, I would like to bring to your attention our Editorial version 1.2:
https://www.geosci-model-dev.net/12/2215/2019/
This highlights some requirements of papers published in GMD, which is also available on the GMD website in the ‘Manuscript Types’ section:
http://www.geoscientific-model-development.net/submission/manuscript_types.html
In particular, please note that for your paper, the following requirement has not been met in the Discussions paper:
- "The main paper must give the model name and version number (or other unique identifier) in the title."
Please add a name and a version number of the model you are publishing here in the title upon your revised submission to GMD. Note, that a name or acroynm and version number make your model much easier referencable.
Yours,
Astrid Kerkweg
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RC1: 'Comment on gmd-2020-423', Anonymous Referee #1, 26 Feb 2021
reply
The present paper aims to include the process of particle coagulation and fragmentation into an OGCM. Particle coagulation is a challenging process to model as it requires the inclusion of many compartments, representing the spectrum of aggregated particle sizes. So far, no real attempt has been made to incorporate this process explicitly into the larger scale biogeochemical models. I believe this is an important step in biological oceanography due to its importance in carbon flux estimations.
Ideally, one would include a total of N tracer compartments, N representing the maximum amount of individual cells or particles a single aggregated floc can contain. However, due to computational restrictions, including N tracers in an OGCM is not feasible. This paper aims to address this problem by reducing the particle size resolution (i.e. reducing the amount of tracers). More importantly, the modelling approach is flexible to allow a nonlinear size distribution, which is more suited to the biological process.
It is encouraging that attempts are being made to incorporate this process into an OGCM and I believe this paper certainly has merit. I would recommend publication subject to addressing the minor concerns raised below.
- Firstly, I was surprised to see links to the cloud microphysics field had not been made. These spectral bin models are used frequently e.g. Khain et al 2004 and a discussion is warranted.
- Line 375 states “But, the sensitivity of our model outcomes to many arbitrary constant parameters needs to be profoundly investigated”. This is an extremely pertinent point and I think at least a basic sensitivity analysis should be conducted.
- Regarding the penalty function: while I appreciate the simplicity used to reconcile the errors arising from nonlinearity, more tests should be carried out to confirm the applicability of the parameter choices used in the function.
- The work aims to replace the simplified coagulation parameterizations currently used in OGCMs, which use several detritus compartments. While I completely agree that the approach used here is a positive step, it would be useful to demonstrate exactly why the model developed here is preferable beyond the current discussion given in the introduction. Is there evidence showing that carbon fluxes are estimated more accurately using this type of method? Can a simple experiment be carried out to show the shortfalls of the other approaches? There is going to be a computational penalty for including more tracers, so it should be shown that the sacrifice is worth it.
- Takeuchi et al. 2019 finds aggregates are bounded in size by the Kolmogorov length scale. Rather than using an arbitrary upper bound, this characteristic could be used to inform the choice of upper bound.
Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A. and Phillips, V., 2004. Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model description and possible applications. Journal of the atmospheric sciences, 61(24), pp.2963-2982.
Takeuchi, M., Doubell, M.J., Jackson, G.A., Yukawa, M., Sagara, Y. and Yamazaki, H., 2019. Turbulence mediates marine aggregate formation and destruction in the upper ocean. Scientific reports, 9(1), pp.1-8.
Gwenaëlle Gremion et al.
Model code and software
Source code and user manual of the Coagfrag Model (Version Version 1) Gremion, G. and Nadeau, L.-P. https://doi.org/10.5281/zenodo.4432896
Gwenaëlle Gremion et al.
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