Ayinde Akeem Shola, Yu Huaming, Wu Kejian
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China.
Physical Oceanography Laboratory, Ocean University of China, Qingdao, 266100, China.
Sci Rep. 2023 Dec 3;13(1):21318. doi: 10.1038/s41598-023-48624-1.
The rising sea levels due to climate change are a significant concern, particularly for vulnerable, low-lying coastal regions like the Gulf of Guinea (GoG). To effectively address this issue, it is crucial to gain a comprehensive understanding of historical sea level variability, and the influencing factors, and develop a reliable modeling system for future projections. This knowledge is essential for informed planning and mitigation strategies aimed at protecting coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020, covering three distinct periods (1993-2002, 2003-2012, and 2013-2020). It investigates the connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Furthermore, the study evaluates the performance of supervised machine learning techniques to optimize sea level modeling. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the northern region, with a total linear trend of 88 mm over the entire period. The highest decadal trend (38.7 mm) emerged during 2013-2020, with the most substantial percentage increment (100%) occurring in 2003-2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. The evaluation of supervised learning modeling methods indicates that Random Forest Regression and Gradient Boosting Machines are the most accurate, reproducing interannual sea level patterns in the GoG with 97% and 96% accuracy. These models could be used to derive regional sea level projections via downscaling of climate models. These findings provide essential insights for effective coastal management and climate adaptation strategies in the GoG.
气候变化导致的海平面上升是一个重大问题，对于几内亚湾（GoG）这样脆弱的低洼沿海地区而言尤其如此。为有效解决这一问题，全面了解历史海平面变化及其影响因素，并开发一个可靠的未来预测建模系统至关重要。这些知识对于制定旨在保护沿海社区和生态系统的明智规划及缓解策略必不可少。本研究全面分析了1993年至2020年几内亚湾平均海平面异常（MSLA）趋势，涵盖三个不同时期（1993 - 2002年、2003 - 2012年和2013 - 2020年）。研究调查了年际海平面变化与大规模海洋和大气强迫之间的联系。此外，该研究评估了监督机器学习技术在优化海平面建模方面的性能。研究结果显示，整个盆地的MSLA线性趋势持续上升，在北部地区尤为明显，整个时期的总线性趋势为88毫米。2013 - 2020年出现了最高的十年趋势（38.7毫米），2003 - 2012年的百分比增幅最大（100%）。十年海平面趋势的空间变化受次盆地物理强迫影响。在空间海平面分布中识别出了强烈的年际信号，这些信号与大规模海洋和大气现象有关。海平面趋势的季节性变化归因于强迫因素的季节性变化。对监督学习建模方法的评估表明，随机森林回归和梯度提升机器最为准确，能够以97%和96%的准确率再现几内亚湾的年际海平面模式。这些模型可通过气候模型的降尺度来推导区域海平面预测。这些发现为几内亚湾有效的海岸管理和气候适应策略提供了重要见解。
