Scientists Develop Novel Model and Dataset for Ocean Dissolved Oxygen Monitoring
Jun 28, 2024
Dissolved oxygen (DO) is a key indicator of seawater quality, and its depletion, known as ocean deoxygenation, poses a significant threat for marine ecosystems and the carbon cycle due to human activities and climate change.
Scientists from the Aerospace Information Research Institute (AIR) at the Chinese Academy of Sciences, along with collaborators, combined global Argo data, artificial intelligence with machine learning techniques to develop a novel method for measuring dissolved oxygen levels in the ocean. This approach offers detailed insights into oxygen variations across diverse locations and timeframes, enhancing our ability to detect and understand changes effectively.
Argo data refers to oceanographic measurements collected by autonomous floats worldwide, providing crucial information on ocean temperature, salinity, and other key variables for scientific research and climate monitoring.
Historically, data on ocean dissolved oxygen from Biogeochemical-Argo have been scarce and unevenly spread out, while satellite estimates have been confined to surface waters. This new model effectively enhances the value of DO data by offering monthly data on dissolved oxygen levels spanning from 2005 to 2022, covering 26 standardized depths. This detailed data unveils how dissolved oxygen is distributed in the deep sea and highlights a rapid expansion of deoxygenated areas.
The model can accurately predict dissolved oxygen levels in the middle to upper ocean using surface data, showing that it works well.
Furthermore, researchers have created a comprehensive global dataset of ocean dissolved oxygen concentration (DOC) over time and space. This dataset combines data from Argo floats collected between 2005 and 2022, offering detailed information on how DOC is distributed throughout the world's oceans.
Traditional datasets are often limited in spatial and temporal coverage. The new dataset solves these issues by combining information from over 2.35 million temperature and salinity profiles collected by Core-Argo floats, along with more than 380,000 profiles of DOC gathered by Biogeochemical-Argo floats. By using advanced machine learning methods, this combination provides a detailed understanding of how dissolved oxygen levels vary across the globe and change over time.
The dataset matches well with existing datasets such as WOA18 and GLODAPv2, especially at depths of 10 and 1000 meters below sea level.
This new resource is anticipated to be useful for several purposes, including monitoring the spread of low-oxygen zones and supporting the Global Ocean Oxygen Decade initiative, which aims to improve how we monitor and predict changes in ocean oxygen levels.
Prof. XUE Cunjin, who led the study at AIR, stated, “With these new models and datasets, scientists are now better equipped to monitor and forecast changes, enabling the development of more effective strategies for safeguarding marine ecosystems."
The research results have been published in the journals including Marine Environmental Research, Remote Sensing, and Geoscience Data Journal.
Contact: luyq@aircas.ac.cn
News & Events