Deep learning reveals hotspots of global oceanic oxygen changes from 2003 to 2020

The decrease in global oceanic dissolved oxygen (DO) has exerted a profound impact on marine ecosystems and biogeochemical processes. However, our comprehension of DO distribution and its global change patterns remains hindered by sparse measurements and coarse-resolution simulations. Here we presented Oxyformer, a deep learning method that accurately learns DO-related information and estimates high-resolution global DO concentration. The results derived by Oxyformer demonstrate an accelerated decline in global oceanic DO content, estimated at approximately 1045 ± 665 Tmol decade −1 from 2003 to 2020. The observed trends exhibit considerable variability across different regions and depths, with some new hotspots of recent DO change including the Equatorial Indian Ocean, the South Pacific Ocean, the North Atlantic Ocean, and the Western Coast of California. The unprecedented modeling approach provides a powerful tool to track changes in global DO contents and to facilitate the understanding of their influences on ocean ecosystems and biogeochemical processes. • Proposing a transformer-based regression model for global oceanic dissolved oxygen. • Verifying the superiority of the model in several ways, e.g., accuracy assessment. • Revealing an accelerated decline trend and some new hotspots of recent change.