OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning

TL;DR

This paper introduces OxyGenerator, a deep learning model that reconstructs a century of global ocean deoxygenation, outperforming traditional simulations by effectively handling sparse data and complex oceanographic correlations.

Contribution

OxyGenerator is the first deep learning approach for century-scale ocean deoxygenation reconstruction, utilizing zoning-varying graph message-passing and uncertainty calibration.

Findings

Reduces MAPE by 38.77% compared to CMIP6 simulations

Successfully captures complex oceanographic correlations

Demonstrates potential for data-driven ocean health assessment

Abstract

Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the "breathless ocean" in data-driven manner.