In this study, we compare mechanistic and empirical approaches to reconstruct the air-sea flux of biological oxygen ((Formula presented.)) by parameterizing the physical oxygen saturation anomaly (ΔO2[phy]) in order to separate the biological contribution from total oxygen. The first approach matches ΔO2[phy] to the monthly climatology of the argon saturation anomaly from a global ocean circulation model's output. The second approach derives ΔO2[phy] from an iterative mass balance model forced by satellite-based physical drivers of ΔO2[phy] prior to the sampling day by assuming that air-sea interactions are the dominant factors driving the surface ΔO2[phy]. The final approach leverages the machine-learning technique of Genetic Programming (GP) to search for the functional relationship between ΔO2[phy] and biophysicochemical parameters. We compile simultaneous measurements of O2/Ar and O2 concentration from 14 cruises to train the GP algorithm and test the validity and applicability of our modeled ΔO2[phy] and (Formula presented.). Among the approaches, the GP approach, which incorporates ship-based measurements and historical records of physical parameters from the reanalysis products, provides the most robust predictions (R2 = 0.74 for ΔO2[phy] and 0.72 for (Formula presented.); RMSE = 1.4% for ΔO2[phy] and 7.1 mmol O2 m−2 d−1 for (Formula presented.)). We use the empirical formulation derived from GP approach to reconstruct regional, inter-annual, and decadal variability of (Formula presented.) based on historical oxygen records. Overall, our study represents a first attempt at deriving (Formula presented.) from snapshot measurements of oxygen, thereby paving the way toward using historical O2 data and a rapidly growing number of O2 measurements on autonomous platforms for independent insight into the biological pump.