Reconstructing Global Monthly Ocean Dissolved Oxygen (1960–2023) to Nearly 6000 m Depth Using Bayesian Ensemble Machine Learning

Abstract. Oceanic oxygen levels, crucial for marine ecosystems and biogeochemical cycles, have declined significantly over the past few decades, driven by climate change and posing severe environmental risks. However, historical dissolved oxygen (DO) measurements, especially below 2000 m, remain sparse, limiting comprehensive annual and seasonal analyses. Here we introduce the BEM-DOR framework, a Bayesian-optimized ensemble of six machine-learning models (Random Forest, XGBoost, LightGBM, CatBoost, Extremely Randomized Trees and Histogram-based Gradient Boosting) fused via dynamic weighting, to reconstruct global monthly DO distributions at 1°×1° resolution from the surface to 5902 m depth over 1960–2023. Validation against an independent dataset demonstrates that BEM-DOR outperforms existing products. Our dataset captures depth-dependent deoxygenation, with the most pronounced decline occurring between 150 and 200 m, and reveals dramatically accelerated oxygen loss in the Arctic Ocean and North Atlantic over the past decade. We quantify uncertainties from measurement errors, gridding processes, and model algorithms, providing the first long-term, high-resolution, uncertainty-quantified DO product from ocean surface to nearly 6000 m depth. The extension of DO data into the bathypelagic zone in this work is a significant contribution to deep ocean oxygen dynamics and global biogeochemical cycles.