Mapping Dissolved Oxygen Concentrations by Combining Shipboard and Argo Observations Using Machine Learning Algorithms
Takamitsu Ito, Ahron Cervania, Kaylin Cross, Sanika Ainchwar, Sara Delawalla
Abstract
Abstract The ocean oxygen (O 2 ) inventory has declined in recent decades but the estimates of O 2 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‐O 2 ) 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 O 2 data sets was generated with similar skills (root‐mean‐square error ∼13–18 μmol/kg and R 2 ∼ 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.