A Black Hole Eddy dataset of North Pacific Ocean based on satellite altimetry

Abstract

The methodologies employed for the identification of coherent ocean eddies can be categorized as either Eulerian and Lagrangian. Among Lagrangian structures, Black Hole Eddies (BHEs) exhibit the highest degree of material coherence and conservation, making them particularly suitable for studying the transport and retention of oceanic materials. This study presents an efficient Graphics Processing Unit (GPU)-based BHE identification algorithm for the North Pacific (0–50° N, 130–270° E), enhancing computational efficiency by approximately 13 times compared to the existing methods. Using this algorithm, a Black Hole Eddy dataset (BHE v1.0) is constructed for the first time in the North Pacific Ocean, based on satellite-derived surface geostrophic velocity data from 1 January 1993 to 5 May 2023. BHE v1.0 contains 18 387 eddies with radius larger than 20 km and lifetimes longer than 4 weeks and captures both the spatial-temporal characteristics and the trajectories of coherent eddies throughout lifetimes. Through the advection of Lagrangian particles in Eulerian eddy, rotationally coherent Lagrangian vortices (RCLVs) and BHEs, it is confirmed that BHEs retain material coherence throughout their lifecycle without significant mixing. Additionally, approximately 6 % of BHEs, which do not overlap with any RCLVs or Eulerian eddies, are identified and referred to as the Naked Black Hole Eddy and further analyze its coherence through advection. Transport analysis shows that BHEs induce westward transport of about 1.5 Sv, three times weaker than the transport attributed to the broader RCLVs, suggesting it likely represents a more accurate estimate of the coherent, non-leaky component of oceanic transport, as BHEs isolate the materially coherent core of the eddies. These findings address the existing gap in Black Hole Eddy datasets within the field of oceanography and provides a novel perspective for studying the interactions between coherent eddies and oceanic physical phenomena. The dataset is publicly available via https://doi.org/10.5281/zenodo.15597447 (Tian et al., 2025a).