Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7143-2023
https://doi.org/10.5194/gmd-16-7143-2023
Model evaluation paper
 | 
08 Dec 2023
Model evaluation paper |  | 08 Dec 2023

An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields

Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm

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Cited articles

Arbic, B. K., Alford, M. H., Ansong, J. K., Buijsman, M. C., Ciotti, R. B., Farrar, J. T., Hallberg, R. W., Henze, C. E., Hill, C. N., Luecke, C. A., Menemenlis, D., Metzger, E. J., Müeller, M., Nelson, A. D., Nelson, B. C., Ngodock, H. E., Ponte, R. M., Richman, J. G., Savage, A. C., Scott, R. B., Shriver, J. F., Simmons, H. L., Souopgui, I., Timko, P. G., Wallcraft, A. J., Zamudio, L., and Zhao, Z.: A Primer on Global Internal Tide and Internal Gravity Wave Continuum Modeling in HYCOM and MITgcm, in: New Frontiers in Operational Oceanography, edited by: Chassignet, E. P., Pascual, A., Tintoré, J., and Verron, J., Chap. 13, GODAE OceanView, 307–392, https://doi.org/10.17125/gov2018.ch13, 2018. a, b
Arbic, B. K., Elipot, S., Brasch, J. M., Menemenlis, D., Ponte, A. L., Shriver, J. F., Yu, X., Zaron, E. D., Alford, M. H., Buijsman, M. C., Abernathey, R., Garcia, D., Guan, L., Martin, P. E., and Nelson, A. D.: Near‐Surface Oceanic Kinetic Energy Distributions From Drifter Observations and Numerical Models, J. Geophys. Res.-Oceans, 127, 1–30, https://doi.org/10.1029/2022JC018551, 2022. a
Bryan, K.: Michael Cox (1941–1989): His Pioneering Contributions to Ocean Circulation, J. Phys. Oceanogr., 21, 1259–1270, 1991. a
Böhm, V. and Seljak, U.: Probabilistic Auto-Encoder, ArXiv [preprint], https://doi.org/10.48550/arXiv.2006.05479, 2020. a
Cheng, S. and Ménard, B.: How to quantify fields or textures? A guide to the scattering transform, arXiv [preprint], https://doi.org/10.48550/arXiv.2112.01288, 2021. a
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Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
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