A machine learning emulator for dissolved oxygen in earth system models

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Abstract
The current generation of earth system models (ESMs) exhibit substantial biases and inter-model spread in representing ocean deoxygenation and the expansion of tropical oxygen minimum zones (OMZs). Improving dissolved oxygen simulation is challenging as it depends on uncertain physical and biogeochemical processes and their interactions. A machine learning-based emulator, O 2 EMU, aims to reduce model biases and inter-model spread by replacing ESMs’ biogeochemical parameterizations with the learned relationships between dissolved oxygen and physical variables. The emulator first learns from the historical shipboard and autonomous observations of dissolved oxygen and temperature and salinity from ocean reanalyses. The learned relationships are then applied to the temperature and salinity outputs from an ensemble of ESM historical simulations. The results show significantly improved climatological OMZ structure and long-term oxygen trends including the shoaling and expansion of the tropical Atlantic OMZs. Application of O 2 EMU to ESM simulations can reduce the inter-model spread by 70%–75% and the root mean square error by 50%–60%, offering a computationally efficient and scalable alternative to standard biogeochemical models, and providing a stepping stone towards hybrid biogeochemical projections that blend mechanistic ESM dynamics with observationally constrained tracer distributions.
Funding Information
  • EU OceanICU (101083922)
  • National Science Foundation (OCE-2123546)
  • U.S. Department of Energy (DE-SC0021300)

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