Sea level variability and modeling in the Gulf of Guinea using supervised machine learning

Akeem Shola Ayinde; Huaming Yu; Kejian Wu

doi:10.1038/s41598-023-48624-1

Sea level variability and modeling in the Gulf of Guinea using supervised machine learning

Sci Rep. 2023 Dec 3;13(1):21318. doi: 10.1038/s41598-023-48624-1.

Authors

Akeem Shola Ayinde^{1

2

3}, Huaming Yu^{4

5}, Kejian Wu^{6

7}

Affiliations

¹ College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China. ayindeas@niomr.gov.ng.
² Physical Oceanography Laboratory, Ocean University of China, Qingdao, 266100, China. ayindeas@niomr.gov.ng.
³ Department of Marine Meteorology and Climate, Nigerian Institute for Oceanography and Marine Research, PMB 12729, Victoria Island, Lagos, Nigeria. ayindeas@niomr.gov.ng.
⁴ College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China. hmyu@ouc.edu.cn.
⁵ Physical Oceanography Laboratory, Ocean University of China, Qingdao, 266100, China. hmyu@ouc.edu.cn.
⁶ College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China.
⁷ Physical Oceanography Laboratory, Ocean University of China, Qingdao, 266100, China.

Abstract

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.