1010 / 2024-09-20 03:38:05

Predictability of Southern Ocean Dissolved Oxygen: Bayesian1 vs. Deterministic Approach to Forecasting2

bayesian approach, dissolved oxygen

Oxygen plays a critical role in the health of marine ecosystems. As oceanic O2 concentration

decreases to hypoxic levels, marine organisms’ habitability decreases rapidly. However, identify

ing the physical patterns driving this reduction in dissolved oxygen remains challenging. This

study employs a Bayesian Neural Network (BNN) to analyze the uncertainty in dissolved oxygen forecasts. The method’s significance lies in its ability to assess oxygen forecasts’ certainty with evolving physical dynamics. The BNN model outperforms traditional linear regression and

persistence methods, particularly under changing climate conditions, where it captures increased uncertainty, as quantified by Bayesian entropy. Our approach leverages three Explainable AI (XAI) techniques—Integrated Gradients, Gradient SHAP, and DeepLIFT—to provide meaningful

interpretations of 2- and 8-year forecasts. The XAI analysis reveals that buoyancy frequency is a critical predictor for short-term forecasts across the North Atlantic Deep Water (NADW), Upper Circumpolar Deep Water (UCDW), and Lower Circumpolar Deep Water (LCDW) masses while

mixing processes and salinity become more influential over longer timescales.