Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques
Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2023.1291232/full |
| _version_ | 1827596198268108800 |
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| author | Sheng Huang Jian Shao Yijun Chen Yijun Chen Jin Qi Jin Qi Sensen Wu Sensen Wu Feng Zhang Feng Zhang Xianqiang He Zhenhong Du Zhenhong Du |
| author_facet | Sheng Huang Jian Shao Yijun Chen Yijun Chen Jin Qi Jin Qi Sensen Wu Sensen Wu Feng Zhang Feng Zhang Xianqiang He Zhenhong Du Zhenhong Du |
| author_sort | Sheng Huang |
| collection | DOAJ |
| description | Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980–2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation. |
| first_indexed | 2024-03-09T03:06:08Z |
| format | Article |
| id | doaj.art-932a450ac51949979d9ca5fb183262c7 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-09T03:06:08Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-932a450ac51949979d9ca5fb183262c72023-12-04T06:55:18ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452023-12-011010.3389/fmars.2023.12912321291232Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniquesSheng Huang0Jian Shao1Yijun Chen2Yijun Chen3Jin Qi4Jin Qi5Sensen Wu6Sensen Wu7Feng Zhang8Feng Zhang9Xianqiang He10Zhenhong Du11Zhenhong Du12School of Earth Sciences, Zhejiang University, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaOceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980–2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation.https://www.frontiersin.org/articles/10.3389/fmars.2023.1291232/fullmeasured dissolved oxygenIndian Oceanocean reanalysis datamachine learningfour-dimensional oxygen reconstructionocean deoxygenation |
| spellingShingle | Sheng Huang Jian Shao Yijun Chen Yijun Chen Jin Qi Jin Qi Sensen Wu Sensen Wu Feng Zhang Feng Zhang Xianqiang He Zhenhong Du Zhenhong Du Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques Frontiers in Marine Science measured dissolved oxygen Indian Ocean ocean reanalysis data machine learning four-dimensional oxygen reconstruction ocean deoxygenation |
| title | Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques |
| title_full | Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques |
| title_fullStr | Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques |
| title_full_unstemmed | Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques |
| title_short | Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques |
| title_sort | reconstruction of dissolved oxygen in the indian ocean from 1980 to 2019 based on machine learning techniques |
| topic | measured dissolved oxygen Indian Ocean ocean reanalysis data machine learning four-dimensional oxygen reconstruction ocean deoxygenation |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2023.1291232/full |
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