Multi-model ocean oxygen fields predicted by conditional diffusion models

Abstract

Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day and future ocean oxygen inventory. To narrow down the uncertainty in estimates of ocean oxygen content, we train a conditional generative diffusion model on outputs of a multi-model ESM ensemble to learn the conditional distribution of upper-ocean oxygen given physical input variables such as temperature and salinity. This generative model has considerable skill in the Atlantic and Southern Oceans, and can generate realistic oxygen samples under conditions not seen in the training data. We validate this model using observational datasets, and use it to generate oxygen fields for models without oxygen data using only temperature and salinity as conditioning inputs. This physics-conditioned extrapolation suggests that model biases in the tropical Pacific Oxygen Minimum Zone may be smaller than currently assumed when considering a larger set of physical ocean states. Our approach provides a complementary way to represent probabilistic multi-model climate distributions.