To the first-order approximation, dissolved oxygen is determined by the competition between ocean ventilation and biological productivity. The oxygen levels vary significantly over space and time, and its pattern is crucial for the marine habitats and cycling of redox-sensitive elements.  There is a growing interest in predicting the oxygen levels in the oceans, and this study examines the Linear Inverse Model (LIM) as a tool to diagnose the variability of dissolved oxygen in the oceans. The variability of oxygen concentration at a point may depend on many factors, but the LIM assumes the evolution of oxygen separated into deterministic and random components. The LIM extracts the linear dynamics from the statistics of (observed or simulated) ocean tracers. In this study, we construct LIM using dissolved oxygen data and related variables to analyze the key processes controlling the variability of dissolved oxygen in the North Pacific. We propose a framework to select the components that are most relevant to the variability of dissolved oxygen.

dissolved oxygen,linear inverse model