资源环境科技发展态势分析平台

Characterizing how ocean turbulence transports heat is critically important for accurately parameterizing global circulation models. We present a novel data-driven approach for identifying distinct regions of turbulent mixing within a microstructure dataset that uses unsupervised machine learning to cluster fluid patches according to their background buoyancy frequency N, and turbulent dissipation rates of kinetic energy ϵ and thermal variance χ. Applied to data collected near the Velasco Reef in Palau, our clustering algorithm discovers spatial and temporal correlations between the mixing characteristics of a fluid patch and its depth, proximity to the reef and the background current. While much of the dataset is characterized by the canonical mixing coefficient Γ = 0.2, elevated local mixing efficiencies are identified in regions containing large density fluxes derived from χ. Once applied to further datasets, unsupervised machine learning has the potential to advance community understanding of global patterns and local characteristics of turbulent mixing.