Unlocking A Global Ocean Mixing Dataset: toward Standardization of Seismic-derived Ocean Mixing Rates

Jingxuan Wei, Zeyu Zhao,Kathryn Gunn, Sean Gulick,Donna Shillington,Christopher Lowery

crossref(2024)

引用 0|浏览0
暂无评分
摘要
Turbulent mixing is vital for water transformation in the ocean and sustains the global thermohaline circulation. Despite decades of global observations using different platforms, our understanding of ocean turbulence is still limited. More observations are needed to better characterize the spatio-temporal distribution of mixing to reduce uncertainties in climate models. Marine seismic reflection surveys are an untapped data resource for high-resolution ocean turbulence observation. Turbulent mixing can be extracted from seismic data through horizontal internal wave slope spectra. However, to date, a standardized approach to prepare seismic data for this spectral analysis is still lacking, leading to insufficient consideration of the impact of noise on the resulting diffusivities. To address these issues, we perform a full-wavefield synthetic modeling and processing to reveal noise-induced overestimation of diffusivities. We further propose a widely applicable workflow and apply it to three field seismic surveys with increasing noise levels conducted in regions of different turbulence environments: ocean ridges, open ocean interior, and continental slope. The derived diffusivities are benchmarked against direct measurements around the region to show the fidelity of this seismic method. The extended observation records by seismic data across the Kauai Channel and away from Mid-Atlantic Ridges reveal the importance of topography in modifying the propagation of internal tides and the distribution of turbulent mixing in both near and far fields. Our proposed workflow marks a key advancement towards standardization of seismic-derived ocean mixing rates and holds the potential to unlock massive marine seismic reflection dataset worldwide for ocean mixing characterization.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要