Estimating Oxygen in the Southern Ocean Using Argo Temperature and Salinity

Abstract

AbstractAn Argo‐based estimate of oxygen (O2) at 150 m is presented for the Southern Ocean (SO) from temperature (T), salinity (S), and O2 Argo profiles collected during 2008–2012. The method is based on a supervised machine learning algorithm known as random forest (RF) regression and provides an estimate for O2 given T, S, location, and time information. The method is validated by attempting to reproduce the Southern Ocean State Estimate (SOSE) O2 field using synthetic data sampled from SOSE. The RF mapping shows skill in the majority of the domain but is problematic in some of the boundary regions. Maps of O2 at 150 m derived from observed profiles suggest that SOSE and the World Ocean Atlas 2013 climatology may overestimate annual mean O2 in the SO, both on a global and basin scale. A large regional bias is found east of Argentina, where high O2 values in the Argo‐based estimate are confined closer to the coast compared to other products. SOSE may also underestimate the annual cycle of O2. Evaluation of the RF‐based method demonstrates its potential to improve understanding of O2 annual mean fields and variability from sparse O2 measurements. This implies the algorithm will also be effective for mapping other biogeochemical variables (e.g., nutrients and carbon). Furthermore, our RF evaluation results can be used to inform the design of future enhancements to the current array of O2 profiling floats.