AWESOME-OCIM: Difference between revisions

Revision as of 11:48, 7 April 2026

Model types › Global scale › AWESOME-OCIM

Page authors: [[User:]]
Responsible curator: [[User:]]

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 build in MATLAB used 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

Data inputs are not necessary when using the AWESOME OCIM. However, parameter inputs for biogeochemical processes are required to ask questions about the biogeochemistry of a tracer. Each source/sink function comes with its own set of parameters that the user must assign a value to. Appendix A of the AWESOME OCIM paper is a great resource that users can use to better understand what real-world process each parameter governs. Parameters are also explained in detail in the srcsnk folder of the downloadable GitHub code. ^[1]

Data inputs are necessary to optimize a model to observations, however. This can be done using MATLAB's fmincon, fminsearch, or fminbound functions. First, data needs to be assimilated onto the AWESOME OCIM shoebox grid, then one of the minimization functions above can change the parameters within the biogeochemical functions to minimize the difference between observations and the model.

Example Studies & Code

Classic examples

Thorough documentation can be found on the original AWESOME OCIM paper. ^[1]

Recent applications

Recent applications include constraining the copper distribution in the ocean, creation of an alkalinity model, and the creation of a global nickel biogeochemical model.

Limitations

References

↑ ^1.0 ^1.1 ^1.2 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
↑ 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

[AO-1] 1.0 ^1.1 ^1.2 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

[2] 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.

[3] 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

[4] 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

[1]

[2]

[3]

[4]

@@ Line 46: / Line 46: @@
 === Recent applications ===
+Recent applications include constraining the copper distribution in the ocean, creation of an alkalinity model, and the creation of a global nickel biogeochemical model.
 == Limitations ==