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Characterizing Ocean Motions Using a Long-Term Observational Dataset


Ocean motions on a scale of less than 20 kilometers, or submesoscale, can be dominated by different types of processes like waves, surface divergence (when water is moving apart), or spiraling movement. Two specific movement patterns which can be hard to distinguish are internal gravity waves (IGWs), which are wave motions traveling between ocean layers, and unbalanced submesoscale motions (USMs), which are spiral patterns which cause surface divergence. In the Gulf Stream region, there is a 14-year long dataset of ocean velocity measurements that provide an opportunity to investigate these patterns. Previous research has concluded that USMs do not play a significant role in this region annually, but did not address any seasonal trends. Work that has been done seasonally has not differentiated between IGWs and USMs. A new study, partially supported by the Climate Program Office’s Climate Variability & Predictability (CVP) Program, uses the available long term dataset to better understand the submesoscale dynamics in the Gulf Stream Region. An international team of researchers, including CVP-supported scientist Baylor Fox-Kemper of Brown University, determined that USMs dominate the submesoscale dynamics in the upper ocean during the winter, while IGWs dominate during the summer. These results, published in the Journal of Physical Oceanography, exemplify the ability to detect USMs from observational velocity data, and the importance of characterizing these motions. This project is part of the research being performed by one of the Climate Process Teams (CPTs) that CVP currently supports. CPTs are interdisciplinary research groups that aim to improve how ocean and atmospheric processes are represented in climate models.

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