Wave-turbulence interaction-induced vertical mixing and its effects in ocean and climate models
AuthorQiao, F; Yuan, Y; Deng, J; Dai, D; Song, Z
Source TitlePhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
University of Melbourne Author/sSong, Zhenya
Document TypeJournal Article
CitationsQiao, F., Yuan, Y., Deng, J., Dai, D. & Song, Z. (2016). Wave-turbulence interaction-induced vertical mixing and its effects in ocean and climate models. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 374 (2065), https://doi.org/10.1098/rsta.2015.0201.
Access StatusOpen Access
Heated from above, the oceans are stably stratified. Therefore, the performance of general ocean circulation models and climate studies through coupled atmosphere-ocean models depends critically on vertical mixing of energy and momentum in the water column. Many of the traditional general circulation models are based on total kinetic energy (TKE), in which the roles of waves are averaged out. Although theoretical calculations suggest that waves could greatly enhance coexisting turbulence, no field measurements on turbulence have ever validated this mechanism directly. To address this problem, a specially designed field experiment has been conducted. The experimental results indicate that the wave-turbulence interaction-induced enhancement of the background turbulence is indeed the predominant mechanism for turbulence generation and enhancement. Based on this understanding, we propose a new parametrization for vertical mixing as an additive part to the traditional TKE approach. This new result reconfirmed the past theoretical model that had been tested and validated in numerical model experiments and field observations. It firmly establishes the critical role of wave-turbulence interaction effects in both general ocean circulation models and atmosphere-ocean coupled models, which could greatly improve the understanding of the sea surface temperature and water column properties distributions, and hence model-based climate forecasting capability.
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