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<p>Oceanic dissolved oxygen (DO) in the ocean has an indispensable role on supporting biological respiration, maintaining ecological balance and promoting nutrient cycling. According to existing research, the total DO has declined by 2% of the total over the past 50 a, and the tropical Pacific Ocean occupied the largest oxygen minimum zone (OMZ) areas. However, the sparse observation data is limited to understanding the dynamic variation and trend of ocean using traditional interpolation methods. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We demonstrate that extreme gradient boosting (XGBoost) model has the best performance, hereby reconstructing a four-dimensional DO dataset of the tropical Pacific Ocean from 1920 to 2023. The results reveal that XGBoost significantly improves the reconstruction performance in the tropical Pacific Ocean, with a 35.3% reduction in root mean-squared error and a 39.5% decrease in mean absolute error. Additionally, we compare the results with three Coupled Model Intercomparison Project Phase 6 (CMIP6) models data to confirm the high accuracy of the 4-dimensional reconstruction. Overall, the OMZ mainly dominates the eastern tropical Pacific Ocean, with a slow expansion. This study used XGBoost to efficiently reconstructing 4-dimensional DO enhancing the understanding of the hypoxic expansion in the tropical Pacific Ocean and we foresee that this approach would be extended to reconstruct more ocean elements.</p>

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Reconstruction of dissolved oxygen in the tropical Pacific Ocean for past 100 years based on XGBoost

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Jingjing Shen,
  • Bin Lu,
  • Lei Zhou,
  • Xiaoying Gan

摘要

Oceanic dissolved oxygen (DO) in the ocean has an indispensable role on supporting biological respiration, maintaining ecological balance and promoting nutrient cycling. According to existing research, the total DO has declined by 2% of the total over the past 50 a, and the tropical Pacific Ocean occupied the largest oxygen minimum zone (OMZ) areas. However, the sparse observation data is limited to understanding the dynamic variation and trend of ocean using traditional interpolation methods. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We demonstrate that extreme gradient boosting (XGBoost) model has the best performance, hereby reconstructing a four-dimensional DO dataset of the tropical Pacific Ocean from 1920 to 2023. The results reveal that XGBoost significantly improves the reconstruction performance in the tropical Pacific Ocean, with a 35.3% reduction in root mean-squared error and a 39.5% decrease in mean absolute error. Additionally, we compare the results with three Coupled Model Intercomparison Project Phase 6 (CMIP6) models data to confirm the high accuracy of the 4-dimensional reconstruction. Overall, the OMZ mainly dominates the eastern tropical Pacific Ocean, with a slow expansion. This study used XGBoost to efficiently reconstructing 4-dimensional DO enhancing the understanding of the hypoxic expansion in the tropical Pacific Ocean and we foresee that this approach would be extended to reconstruct more ocean elements.