AWESOME-OCIM
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| Model type |
|---|
| Approach: Mechanistic optimized |
| Computational demand: Local |
| Spatial resolution: 3D shoebox: 2° latitude by 2° longitude, 24 uneven depth layers |
| Temporal resolution: n/a |
Model overview
A Working Environment for Simulating Ocean Movement and Elemental cycling with an Ocean Circulation Inverse Model (AWESOME OCIM) is a tool for simulating ocean movement and tracer biogeochemical properties within a transport matrix model (TMM) framework.[1] The circulation of the ocean is described in the form of a matrix where the ocean is discretized into a 3D shoebox by latitude, longitude, and depth. The circulation matrix is based off of a study by DeVries and Preimeau in which they sought to constrain estimates of water-mass distributions and ages in the global ocean. [2]
The AWESOME OCIM is a steady-state modeling tool, meaning there is no time component when modeling tracer distribution. This results in some limitations for its use, however, an advantage to using this tool is that it can complete runs on a local computer in as little as 4 seconds.
Biogeochemical processes are represented by a series of linear equations. This may sound intimidating to non-math folk, however the processes are written in a modular format that guides users clearly on how to input parameters and create models. Sources and sinks of tracers include dust, hydrothermal vents, biological cycling, and scavenging.
Scales of interest
The AWESOME OCIM was developed for studying the steady-state distributions of tracers and their isotopes in the ocean. Biogeochemical functions can be modified to ask specific questions about regional inputs/outputs, but this modeling tool works best at a global scale.
Typical usage is to investigate steady-state trace metal cycling in the ocean.[3] [4]
Data inputs
Example Studies & Code
Classic examples
Recent applications
Limitations
References
- ↑ John, S. G., Liang, H., Weber, T., DeVries, T., Primeau, F., Moore, K., Holzer, M., Mahowald, N., Gardner, W., Mishonov, A., Richardson, M. J., Faugere, Y., & Taburet, G. (2020). AWESOME OCIM: A simple, flexible, and powerful tool for modeling elemental cycling in the oceans. Chemical Geology, 533, 119403. https://doi.org/10.1016/j.chemgeo.2019.119403
- ↑ DeVries, T., & Primeau, F. (2011). Dynamically and observationally constrained estimates of water-mass distributions and ages in the global ocean. Journal of Physical Oceanography, 41(12), 2381–2401.
- ↑ John, S. G., Kelly, R. L., Bian, X., Fu, F., Smith, M. I., Lanning, N. T., Liang, H., Pasquier, B., Seelen, E. A., Holzer, M., Wasylenki, L., Conway, T. M., Fitzsimmons, J. N., Hutchins, D. A., & Yang, S.-C. (2022). The biogeochemical balance of oceanic nickel cycling. Nature Geoscience, 15(11), 906–912. https://doi.org/10.1038/s41561-022-01045-7
- ↑ DeVries, T., & Primeau, F. (2011). Dynamically and observationally constrained estimates of water-mass distributions and ages in the global ocean. Journal of Physical Oceanography, 41(12), 2381–2401. John, S. G., Kelly, R. L., Bian, X., Fu, F., Smith, M. I., Lanning, N. T., Liang, H., Pasquier, B., Seelen, E. A., Holzer, M., Wasylenki, L., Conway, T. M., Fitzsimmons, J. N., Hutchins, D. A., & Yang, S.-C. (2022). The biogeochemical balance of oceanic nickel cycling. Nature Geoscience, 15(11), 906–912. https://doi.org/10.1038/s41561-022-01045-7 John, S. G., Liang, H., Weber, T., DeVries, T., Primeau, F., Moore, K., Holzer, M., Mahowald, N., Gardner, W., Mishonov, A., Richardson, M. J., Faugere, Y., & Taburet, G. (2020). AWESOME OCIM: A simple, flexible, and powerful tool for modeling elemental cycling in the oceans. Chemical Geology, 533, 119403. https://doi.org/10.1016/j.chemgeo.2019.119403 Liang, H., Moffett, J. W., & John, S. (2020). Constraining the Global Marine Copper Cycle through a Data-based Modeling Approach. Ocean Sciences Meeting 2020. Liang, H., Moffett, J. W., & John, S. G. (2023). Toward a Better Understanding of the Global Ocean Copper Distribution and Speciation Through a Data-Constrained Model. Global Biogeochemical Cycles, 37(9), e2023GB007769. https://doi.org/10.1029/2023GB007769