The ocean oxygen (O2) inventory has declined in recent decades but the estimates of O2 trend are uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed using machine learning techniques and biogeochemical Argo array. The source data includes historical shipboard (bottle and CTD-O2) profiles from 1965 to 2020 and biogeochemical Argo profiles after 2005. Neural network and random forest algorithms were trained using approximately 80 % of this data and the remaining 20% for validation. The training data is further divided into 5-fold decadal groups to perform cross validation and hyperparameter tuning. Through different combinations of algorithm types and predictor variable sets, an ensemble of gridded monthly O2 datasets was generated with similar skills (root-mean-square error ~ 13-18 micro-mol/kg and R2 ~ 0.9). The largest errors are found in the oxycline and frontal regions with strong lateral and vertical gradients. The mapping was repeated with shipboard data only and with both shipboard and Argo data. The effect of including Argo data on the estimated global deoxygenation trends has a major impact with an 56% increase while reducing the uncertainty by 40% as measured by the ensemble spread. This study demonstrates the importance of new biogeochemical Argo arrays in relatively data-poor regions such as the Southern Ocean.

The ocean oxygen (O2) inventory has declined in recent decades but the estimates of O2 trend is uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed for the North Atlantic basin using machine learning techniques and biogeochemical Argo array. The source data includes 159 thousand historical shipboard (bottle and CTD-O2) profiles from 1965 to 2020 and 17 thousand Argo O2 profiles after 2005. Neural network and random forest algorithms were trained using 80% of this data using different hyperparameters and predictor variable sets. From a total of 240 trained algorithms, 12 high performing algorithms were selected based on their ability to accurately predict the 20% of oxygen data withheld from training. The final product includes gridded monthly O2 ensembles with similar skills (mean bias < 1mol/kg and R2 > 0.95). The reconstruction of basin-scale oxygen inventory shows a moderate increase before 1980 and steep decline after 1990 in agreement with a previous estimate using an optimal interpolation method. However, significant differences exist between reconstructions trained with only shipboard data and with both shipboard and Argo data. The gridded oxygen datasets using only shipboard measurements resulted in a wide spread of deoxygenation trends (0.8-2.7% per decade) during 1990-2010. When both shipboard and Argo were used, the resulting deoxygenation trends converged within a smaller spread (1.4-2.0% per decade). This study demonstrates the importance of new biogeochemical Argo arrays in combination with applications of machine learning techniques.