An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary
Atmospheric pressure gradients determine the dynamics of the southwest monsoon (SWM) and northeast monsoon (NEM), resulting in rainfall in the Indian subcontinent. Consequently, the surface salinity, mixed layer, and thermocline are impacted by the seasonal freshwater outflow and direct rainfall. Mo...
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2025-06-01
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| author | Harunur Rashid Xiaohui He Yang Wang C. K. Shum Min Zeng |
| author_facet | Harunur Rashid Xiaohui He Yang Wang C. K. Shum Min Zeng |
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| description | Atmospheric pressure gradients determine the dynamics of the southwest monsoon (SWM) and northeast monsoon (NEM), resulting in rainfall in the Indian subcontinent. Consequently, the surface salinity, mixed layer, and thermocline are impacted by the seasonal freshwater outflow and direct rainfall. Moreover, seasonally reversing monsoon gyre and associated currents govern the northern Indian Ocean surface oceanography. This study provides an overview of the impact of these dynamic changes on sea surface temperature, salinity, and productivity by integrating more than 3000 planktonic foraminiferal censuses and bulk sediment geochemical data from sediment core tops, plankton tows, and nets between 25° N and 10° S and 40° E and 110° E of the past six decades. These data were used to construct spatial maps of the five most dominant planktonic foraminifers and illuminate their underlying environmental factors. Moreover, the cured foraminiferal censuses and the modern oceanographic data were used to test the newly developed artificial neural network (ANN) algorithm to calculate the relationship with modern water column temperatures (WCTs). Furthermore, the tested relationship between the ANN derived models was applied to two foraminiferal censuses from the northern Bay of Bengal core MGS29-GC02 (13°31′59″ N; 91°48′21″ E) and the southern Bay of Bengal Ocean Drilling Program (ODP) Site 758 (5°23.05′ N; 90°21.67′ E) to reconstruct the WCTs of the past 890 ka. The reconstructed WCTs at the 10 m water depth of core GC02 suggest dramatic changes in the sea surface during the deglacial periods (i.e., Bolling–Allerǿd and Younger Dryas) compared to the Holocene. The WCTs at Site 758 indicate a shift in the mixed-layer summer temperature during the past 890 ka at the ODP Site, in which the post-Mid-Brunhes period (at 425 ka) was overall warmer than during the prior time. However, the regional alkenone-derived sea-surface temperatures (SSTs) do not show such a shift in the mixed layer. Therefore, this study hypothesizes that the divergence in regional SSTs is most likely due to differences in seasonality and depth habitats in the paleo-proxies. |
| format | Article |
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| institution | Kabale University |
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| publishDate | 2025-06-01 |
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| spelling | doaj-art-2c075fd85c8c4b1f896ff9d6498077f62025-08-20T03:58:27ZengMDPI AGGeosciences2076-32632025-06-0115724110.3390/geosciences15070241An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late QuaternaryHarunur Rashid0Xiaohui He1Yang Wang2C. K. Shum3Min Zeng4School of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Earth Sciences, Ohio State University, Columbus, OH 43201, USACollege of Life Sciences, Jiangxi Science and Technology Normal University, Nanchang 330038, ChinaAtmospheric pressure gradients determine the dynamics of the southwest monsoon (SWM) and northeast monsoon (NEM), resulting in rainfall in the Indian subcontinent. Consequently, the surface salinity, mixed layer, and thermocline are impacted by the seasonal freshwater outflow and direct rainfall. Moreover, seasonally reversing monsoon gyre and associated currents govern the northern Indian Ocean surface oceanography. This study provides an overview of the impact of these dynamic changes on sea surface temperature, salinity, and productivity by integrating more than 3000 planktonic foraminiferal censuses and bulk sediment geochemical data from sediment core tops, plankton tows, and nets between 25° N and 10° S and 40° E and 110° E of the past six decades. These data were used to construct spatial maps of the five most dominant planktonic foraminifers and illuminate their underlying environmental factors. Moreover, the cured foraminiferal censuses and the modern oceanographic data were used to test the newly developed artificial neural network (ANN) algorithm to calculate the relationship with modern water column temperatures (WCTs). Furthermore, the tested relationship between the ANN derived models was applied to two foraminiferal censuses from the northern Bay of Bengal core MGS29-GC02 (13°31′59″ N; 91°48′21″ E) and the southern Bay of Bengal Ocean Drilling Program (ODP) Site 758 (5°23.05′ N; 90°21.67′ E) to reconstruct the WCTs of the past 890 ka. The reconstructed WCTs at the 10 m water depth of core GC02 suggest dramatic changes in the sea surface during the deglacial periods (i.e., Bolling–Allerǿd and Younger Dryas) compared to the Holocene. The WCTs at Site 758 indicate a shift in the mixed-layer summer temperature during the past 890 ka at the ODP Site, in which the post-Mid-Brunhes period (at 425 ka) was overall warmer than during the prior time. However, the regional alkenone-derived sea-surface temperatures (SSTs) do not show such a shift in the mixed layer. Therefore, this study hypothesizes that the divergence in regional SSTs is most likely due to differences in seasonality and depth habitats in the paleo-proxies.https://www.mdpi.com/2076-3263/15/7/241Indian monsoonsforaminiferal censuswater column temperaturesalkenone temperatureartificial intelligence |
| spellingShingle | Harunur Rashid Xiaohui He Yang Wang C. K. Shum Min Zeng An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary Geosciences Indian monsoons foraminiferal census water column temperatures alkenone temperature artificial intelligence |
| title | An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary |
| title_full | An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary |
| title_fullStr | An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary |
| title_full_unstemmed | An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary |
| title_short | An Overview of the Indian Monsoon Using Micropaleontological, Geochemical, and Artificial Neural Network (ANN) Proxies During the Late Quaternary |
| title_sort | overview of the indian monsoon using micropaleontological geochemical and artificial neural network ann proxies during the late quaternary |
| topic | Indian monsoons foraminiferal census water column temperatures alkenone temperature artificial intelligence |
| url | https://www.mdpi.com/2076-3263/15/7/241 |
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