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 14 Nov 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
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.