Hybrid Graph Learning Reconstructs Global Ocean Oxygen Spatiotemporal Changes
TL;DR
Jingwei is a 4D spatiotemporal graph transfer learning model that reconstructs ocean oxygen levels globally over the past six decades on a 1◦ x 1◦ grid, covering depths of 0-5500 meters, and foresee Jingwei revolutionizing observation-based ocean modeling and deepening the authors' understanding of the breathless ocean.
Abstract
Climate change and anthropogenic activities have exacerbated hypoxic conditions in the global ocean, posing a serious threat to marine ecosystems. Quantifying changes in dissolved oxygen levels is crucial for understanding the impact of these factors on marine life and earth sustainability. However, dissolved oxygen observations are severely sparse, limiting comprehensive analysis. Here we present Jingwei, a 4D spatiotemporal graph transfer learning model that reconstructs ocean oxygen levels globally over the past six decades on a 1◦ x 1◦ grid, covering depths of 0-5500 meters. Jingwei simultaneously leverages intra-profile knowledge transfer using a pattern bank from simulations and captures inter-profile correlations through zoning-varying message passing among observations. It significantly reduces reconstruction error by 27.26% compared to CMIP6, demonstrates high consistency with cruise surveys, and provides satisfactory visual quality. Furthermore, Jingwei produces interpretable results, effectively identifying vertical profile patterns and distinguishing fine-grained spatial distributions. Jingwei provides cartography and quantitative analysis of oxygen minimum zones (OMZ) evolution since 1960. We foresee Jingwei revolutionizing observation-based ocean modeling and deepening our understanding of the breathless ocean. Alongside, we have released an open-source online platform (https://jingwei.acemap.info/ https://jingwei.acemap.info/), providing data visualization, resource sharing and ongoing updates.