Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8683-2024
https://doi.org/10.5194/gmd-17-8683-2024
Development and technical paper
 | 
10 Dec 2024
Development and technical paper |  | 10 Dec 2024

An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves

Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang

Related authors

MITgcm-RN v1.0: Modeling the Transport and Fate of Radionuclides Released from Nuclear Power Plants Wastewater in the Global Ocean Using MITgcm_c65i with the Radionuclide Module
Mao Mao, Yujuan Wang, Peipei Wu, Shaojian Huang, Zhengcheng Song, and Yanxu Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3307,https://doi.org/10.5194/egusphere-2025-3307, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025,https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024,https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023,https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0
Xiaotian Xu, Xu Feng, Haipeng Lin, Peng Zhang, Shaojian Huang, Zhengcheng Song, Yiming Peng, Tzung-May Fu, and Yanxu Zhang
Geosci. Model Dev., 15, 3845–3859, https://doi.org/10.5194/gmd-15-3845-2022,https://doi.org/10.5194/gmd-15-3845-2022, 2022
Short summary

Related subject area

Biogeosciences
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Benjamin F. Meyer, João P. Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025,https://doi.org/10.5194/gmd-18-4643-2025, 2025
Short summary
pyVPRM: a next-generation vegetation photosynthesis and respiration model for the post-MODIS era
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
Geosci. Model Dev., 18, 4713–4742, https://doi.org/10.5194/gmd-18-4713-2025,https://doi.org/10.5194/gmd-18-4713-2025, 2025
Short summary
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025,https://doi.org/10.5194/gmd-18-4317-2025, 2025
Short summary
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025,https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025,https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary

Cited articles

Amos, H. M., Sonke, J. E., Obrist, D., Robins, N., Hagan, N., Horowitz, H. M., Mason, R. P., Witt, M., Hedgecock, I. M., Corbitt, E. S., and Sunderland, E. M.: Observational and Modeling Constraints on Global Anthropogenic Enrichment of Mercury, Environ. Sci. Technol., 49, 4036–4047, https://doi.org/10.1021/es5058665, 2015. 
Andersson, M. E., Gårdfeldt, K., Wängberg, I., and Strömberg, D.: Determination of Henry's law constant for elemental mercury, Chemosphere, 73, 587–592, https://doi.org/10.1016/j.chemosphere.2008.05.067, 2008. 
Asher, W., Edson, J., Mcgillis, W., Wanninkhof, R., Ho, D. T., and Litchendor, T.: Fractional Area Whitecap Coverage and Air-Sea Gas Transfer Velocities Measured During GasEx-98, in: Geophysical Monograph Series, edited by: Donelan, M. A., Drennan, W. M., Saltzman, E. S., and Wanninkhof, R., American Geophysical Union, Washington, D. C., 199–203, https://doi.org/10.1029/GM127p0199, 2002. 
Asher, W. E. and Wanninkhof, R.: The effect of bubble-mediated gas transfer on purposeful dual-gaseous tracer experiments, J. Geophys. Res., 103, 10555–10560, https://doi.org/10.1029/98JC00245, 1998. 
Asher, W. E., Karle, L. M., Higgins, B. J., Farley, P. J., Monahan, E. C., and Leifer, I. S.: The influence of bubble plumes on air-seawater gas transfer velocities, J. Geophys. Res., 101, 12027–12041, https://doi.org/10.1029/96JC00121, 1996. 
Download
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Share