the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Incorporating Recalcitrant Dissolved Organic Carbon and Microbial Carbon Pump Processes into the cGENIE Earth System Model (cGENIEv0.9.35-MCP)
Abstract. Recalcitrant dissolved organic carbon (RDOC) is a significant component of dissolved organic carbon (DOC), produced through the microbial carbon pump (MCP), and plays a crucial role in long-term carbon sequestration. In this study, we extend the cGENIE Earth System Model by integrating the RDOC fraction and embedding MCP-driven transformations, resulting in the enhanced cGENIE-MCP model. We implement temperature-dependent limitations on nutrient uptake and organic matter remineralization to simulate MCP processes. Model outputs are compared with contemporary observations and previous cGENIE versions. The model effectively simulates the spatial distribution of concentrations and production rates of labile (LDOC), semi-labile (SLDOC), and RDOC. The cGENIE-MCP model demonstrates improved accuracy over previous versions, capturing spatial variability in DOC pools and quantifying MCP contributions to long-term carbon sequestration. For instance, sea surface DOC concentrations exhibit a latitudinal gradient, with values ranging from 65–80 μmol kg-1 in tropical-subtropical zones to 40–50 μmol kg-1 in subpolar regions. RDOC concentrations remain relatively stable at 40–50 μmol kg-1 throughout the water column, while LDOC and SLDOC concentrations are typically below 10 μmol kg-1 and 34 μmol kg-1, respectively, in high-production areas. The model reveals a strong spatial correlation between primary production and LDOC production in upwelling zones, while RDOC production exhibits long-term carbon sequestration. These results emphasize the importance of incorporating MCP processes into Earth system models to better predict ocean carbon sink efficiency and biogeochemical responses to climate change. The cGENIE-MCP model provides a tool for studying the dynamics of ocean DOC and carbon cycle across timescales from paleo to future projections.
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Status: open (until 17 Dec 2025)
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EC1: 'Comment on egusphere-2025-2967', Paul Halloran, 25 Sep 2025
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AC1: 'Reply on EC1', Wentao Ma, 25 Sep 2025
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Thank you for your careful reading and helpful comment. We confirm that the settings of the standard cGENIE and cGENIE-MCP are different. In the standard cGENIE version (without MCP or RDOC processes), the MCP-related tracers were switched off, for example:
gm_ocn_select_15=.true. # DOM_C
gm_ocn_select_18=.true. # DOM_N
gm_ocn_select_20=.true. # DOM_P
gm_ocn_select_22=.true. # DOM_Fe
gm_ocn_select_67=.false. # RDOM_C
gm_ocn_select_70=.false. # RDOM_N
gm_ocn_select_72=.false. # RDOM_P
gm_ocn_select_110=.false. # URDOM_C
gm_ocn_select_111=.false. # URDOM_N
gm_ocn_select_112=.false. # URDOM_P
In contrast, for the cGENIE-MCP version these tracers were activated:
gm_ocn_select_15=.true. # DOM_C
gm_ocn_select_18=.true. # DOM_N
gm_ocn_select_20=.true. # DOM_P
gm_ocn_select_22=.true. # DOM_Fe
gm_ocn_select_67=.true. # RDOM_C
gm_ocn_select_70=.true. # RDOM_N
gm_ocn_select_72=.true. # RDOM_P
gm_ocn_select_110=.true. # URDOM_C
gm_ocn_select_111=.true. # URDOM_N
gm_ocn_select_112=.true. # URDOM_P
The corresponding parameter values are then specified in separate user configuration files. To run the two model versions, we used different commands with different configuration files.
To prevent any possible misunderstanding, we have revised the README to explicitly include these configuration details and example run commands. We will update the code on zenodo after receiving comments from reviewers.
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AC1: 'Reply on EC1', Wentao Ma, 25 Sep 2025
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RC1: 'Comment on egusphere-2025-2967', Jamie Wilson, 26 Sep 2025
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2967/egusphere-2025-2967-RC1-supplement.pdf
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AC2: 'Reply on RC1', Wentao Ma, 23 Oct 2025
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We would like to thank the reviewer for his thorough evaluation of our manuscript and for the constructive comments and suggestions. We have carefully revised the manuscript according to the comments.
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AC3: 'Reply on AC2', Wentao Ma, 23 Oct 2025
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We would like to thank the reviewer for his thorough evaluation of our manuscript and for the constructive comments and suggestions. We have carefully revised the manuscript according to the comments. Please see our reply in the attached supplement.
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AC3: 'Reply on AC2', Wentao Ma, 23 Oct 2025
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AC2: 'Reply on RC1', Wentao Ma, 23 Oct 2025
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RC2: 'Comment on egusphere-2025-2967', Anonymous Referee #2, 12 Dec 2025
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This submission describes a new version of GENIE that explicitly represents recalcitrant DOC with a microbial degradation pathway from labile organic matter to successively refractory DOC pools. The authors justify their work by noting that DOC is an important pool of reduced carbon in the ocean that is not explicitly represented in state of the art CMIP models. More importantly, they note that the situation is the same in cGENIE, citing MESMO 3. These are earth system models of intermediate complexity that are more suited for long term climate simulations. The authors argue that the novelty of their work is their incorporation of a calibrated DOC cycle in a model within the GENIE family of models. Their new model is called cGENIE-MCP.
It is unfortunate that the authors have not apparently done a literature survey. Gilchrist & Matsumoto (2023) in Paleoceanography and Paleoclimatology have published MESMO 3c, in which they have already done what the authors of this submission claim to do for the first time. MEMSO 3c has explicit DOC transformation pathways with environmental dependence and is calibrated against available DOC data. There are some features of DOC cycling such as DOC degradation in hydrothermal vents that are represented in MESMO 3c but is not in cGENIE-MCP. Gilchrist & Matsumoto (2023) have even carried out a glacial DOC cycle study, a long term study that the authors of this submission hope to do.
The authors will have to go back to the drawing board and figure out what's new in their work that would justify its publication.
More generally, the authors need to do due diligence in carrying out a comprehensive literature survey, understand how their work fit in the history of this field, and fully acknowledge prior work. For example, the authors note that MESMO 3 is "derived from cGENIE." I don't believe this is true. "cGENIE" did not exist when MESMO was first described. GENIE-1 did. Also, the reference given for MESMO 3 is a Discussion paper. There is another Discussion paper by Lauvset et al. referenced. The correct references should be their accepted versions.
I'll also make a few more comments. First, the reference to CMIP6 sounds like a strawman argument, because as the authors noted, CMIP models are used in "near-term climate projections." It really doesn't matter whether these models have refractory DOC or not. Second, none of the equations are labelled. I would assume that GMD would require labelling. Third and more substantively, the model description is unclear on what is new and what is legacy code. Did these authors really modify air-sea gas exchange that requires explicit description (section 2.2.1)? If not, as I presume, why have 2.2.1, when there are many more aspects to biogeochemical modeling than what is contained in the entire section 2.2? Fourth, Table 1 contains important model parameters, and yet there is not a single call-out of Table 1 in the text. Fifth, Table 2 is not useful. It seems to be a global comparison, but the deep ocean is not highly variable. A global comparison would be biased toward the deep (i.e., global mean) just because of it large volume. Surface and intermediate depth comparisons would be more useful. And why does Table 2 include temperature and salinity? As far as I can tell, cGENIE-MCP has the same model physics as cGENIE.
Citation: https://doi.org/10.5194/egusphere-2025-2967-RC2
Model code and software
cGENIE-MCP Wentao Ma and Yuxian Lai https://github.com/wentaoma12/cGENIE-MCP
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Question from the editor.
From the provided code and README it is not clear that you have not run two instances of your new model rather than the original model and the new model – I.e. it does not show how you can turn off their new features in the code for the original run. The very similar results between the two models could for example arise from comparing two stages of the same spin-up, from mistakenly running the same model twice as one may assume from the provided code and README. This is likley a simple communication issue, but please can you check whether or not this was the case and if it were not, respond here and update the documents and explain technically how the two experiments were performed.
Thank you.