  Accurately reconstructing the global ocean deoxygenation over a century is
crucial for assessing and protecting marine ecosystem. Existing
expert-dominated numerical simulations fail to catch up with the dynamic
variation caused by global warming and human activities. Besides, due to the
high-cost data collection, the historical observations are severely sparse,
leading to big challenge for precise reconstruction. In this work, we propose
OxyGenerator, the first deep learning based model, to reconstruct the global
ocean deoxygenation from 1920 to 2023. Specifically, to address the
heterogeneity across large temporal and spatial scales, we propose
zoning-varying graph message-passing to capture the complex oceanographic
correlations between missing values and sparse observations. Additionally, to
further calibrate the uncertainty, we incorporate inductive bias from dissolved
oxygen (DO) variations and chemical effects. Compared with in-situ DO
observations, OxyGenerator significantly outperforms CMIP6 numerical
simulations, reducing MAPE by 38.77%, demonstrating a promising potential to
understand the "breathless ocean" in data-driven manner.
