Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2649-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2649-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 May 2023
Model description paper |
| 17 May 2023
| 17 May 2023
The 3D biogeochemical marine mercury cycling model MERCY v2.0 – linking atmospheric Hg to methylmercury in fish
Johannes Bieser
CORRESPONDING AUTHOR
Institute of Coastal Systems – Analysis and Modeling, Helmholtz-Zentrum Hereon,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
David J. Amptmeijer
Institute of Coastal Systems – Analysis and Modeling, Helmholtz-Zentrum Hereon,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Ute Daewel
Institute of Coastal Systems – Analysis and Modeling, Helmholtz-Zentrum Hereon,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Joachim Kuss
Department of Marine Chemistry, Leibniz Institute for Baltic Sea Research, Seestraße 15, 18119 Rostock, Germany
Anne L. Soerensen
Department of Environmental
Research and Monitoring, Swedish Museum of Natural History, Stockholm, Sweden
Corinna Schrum
Institute of Coastal Systems – Analysis and Modeling, Helmholtz-Zentrum Hereon,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Institute of Oceanography, Universität Hamburg, Mittelweg
177, 20146 Hamburg, Germany
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Cited
16 citations as recorded by crossref.
- Numerical modeling of dissolved mercury dynamics and transformation in sea water in Minamata Bay, Japan J. Wang et al. https://doi.org/10.1016/j.marpolbul.2025.119196
- Mercury cycling in contaminated coastal environments: modeling the benthic-pelagic coupling and microbial resistance in the Venice Lagoon G. Rosati et al. https://doi.org/10.1016/j.watres.2024.121965
- Bioconcentration as a key driver of Hg bioaccumulation in high-trophic-level fish D. Amptmeijer & J. Bieser https://doi.org/10.5194/bg-22-7425-2025
- Neither Cyclic HgO3 nor Biradicaloid OHgOO Participate in the Gas-phase Oxidation of Hg(0) by Ozone D. Hewa Edirappulige et al. https://doi.org/10.1021/acs.jpca.6c02693
- DOM consumption and demethylation of MeHg as potential drivers of low MeHg in Mediterranean Sea sponges and benthic fish: a modeling perspective D. Amptmeijer et al. https://doi.org/10.5194/bg-23-4057-2026
- Numerical models for monitoring and forecasting ocean biogeochemistry: a short description of present status G. Cossarini et al. https://doi.org/10.5194/sp-5-opsr-12-2025
- Feeding strategy as a key driver of the bioaccumulation of MeHg in megabenthos D. Amptmeijer et al. https://doi.org/10.5194/bg-22-7483-2025
- Incubation Experiments, Observations, and Modeling Highlight the Key Role of Dimethylmercury on Seawater Methylmercury Distributions A. Kleindienst et al. https://doi.org/10.1021/acs.est.5c01491
- Modelling the anthropogenic Hg pollution fingerprint on the marine fishery production worldwide: A preliminary exposure assessment for people living in countries having different income levels F. De Simone et al. https://doi.org/10.1016/j.envint.2024.108891
- The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy A. Dastoor et al. https://doi.org/10.5194/gmd-18-2747-2025
- A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry S. Zhu et al. https://doi.org/10.5194/gmd-16-5915-2023
- Salinity-driven stratification enhances riverine mercury export to the coastal ocean R. Ovbiebo et al. https://doi.org/10.1016/j.ecss.2025.109595
- Bioaccumulation as a driver of high MeHg in the North and Baltic Seas D. Amptmeijer et al. https://doi.org/10.5194/bg-22-7929-2025
- Future mercury levels in fish: model vs. observational predictions under different policy scenarios H. Gull et al. https://doi.org/10.5194/bg-23-3387-2026
- Speciation-driven toxicity and remediation of mercury: Mechanistic insights and policy implications N. Shah et al. https://doi.org/10.1016/j.hazadv.2026.101233
- Formation of Refractory Monomethylmercury Pools by Particle Adsorption S. Gindorf et al. https://doi.org/10.1021/acsestwater.6c00243
16 citations as recorded by crossref.
- Numerical modeling of dissolved mercury dynamics and transformation in sea water in Minamata Bay, Japan J. Wang et al. https://doi.org/10.1016/j.marpolbul.2025.119196
- Mercury cycling in contaminated coastal environments: modeling the benthic-pelagic coupling and microbial resistance in the Venice Lagoon G. Rosati et al. https://doi.org/10.1016/j.watres.2024.121965
- Bioconcentration as a key driver of Hg bioaccumulation in high-trophic-level fish D. Amptmeijer & J. Bieser https://doi.org/10.5194/bg-22-7425-2025
- Neither Cyclic HgO3 nor Biradicaloid OHgOO Participate in the Gas-phase Oxidation of Hg(0) by Ozone D. Hewa Edirappulige et al. https://doi.org/10.1021/acs.jpca.6c02693
- DOM consumption and demethylation of MeHg as potential drivers of low MeHg in Mediterranean Sea sponges and benthic fish: a modeling perspective D. Amptmeijer et al. https://doi.org/10.5194/bg-23-4057-2026
- Numerical models for monitoring and forecasting ocean biogeochemistry: a short description of present status G. Cossarini et al. https://doi.org/10.5194/sp-5-opsr-12-2025
- Feeding strategy as a key driver of the bioaccumulation of MeHg in megabenthos D. Amptmeijer et al. https://doi.org/10.5194/bg-22-7483-2025
- Incubation Experiments, Observations, and Modeling Highlight the Key Role of Dimethylmercury on Seawater Methylmercury Distributions A. Kleindienst et al. https://doi.org/10.1021/acs.est.5c01491
- Modelling the anthropogenic Hg pollution fingerprint on the marine fishery production worldwide: A preliminary exposure assessment for people living in countries having different income levels F. De Simone et al. https://doi.org/10.1016/j.envint.2024.108891
- The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy A. Dastoor et al. https://doi.org/10.5194/gmd-18-2747-2025
- A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry S. Zhu et al. https://doi.org/10.5194/gmd-16-5915-2023
- Salinity-driven stratification enhances riverine mercury export to the coastal ocean R. Ovbiebo et al. https://doi.org/10.1016/j.ecss.2025.109595
- Bioaccumulation as a driver of high MeHg in the North and Baltic Seas D. Amptmeijer et al. https://doi.org/10.5194/bg-22-7929-2025
- Future mercury levels in fish: model vs. observational predictions under different policy scenarios H. Gull et al. https://doi.org/10.5194/bg-23-3387-2026
- Speciation-driven toxicity and remediation of mercury: Mechanistic insights and policy implications N. Shah et al. https://doi.org/10.1016/j.hazadv.2026.101233
- Formation of Refractory Monomethylmercury Pools by Particle Adsorption S. Gindorf et al. https://doi.org/10.1021/acsestwater.6c00243
Saved (final revised paper)
Latest update: 30 Sep 2026
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant...
