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<p>Sea Surface Temperature (SST) variability in the Gulf of Guinea (GoG) significantly influences the regional climate of West Africa and the marine ecosystem of the Atlantic Ocean. This study examines both high-frequency (seasonal to sub-seasonal) and low-frequency (interannual to interdecadal) climatic modes contributing to SST variability in the GoG. Findings indicate that the El Niño Southern Oscillation is associated with SST variability in the GoG. Nonetheless, the Tropical South Atlantic Ocean’s SST patterns are the primary driver of the GoG’s seasonal to sub-seasonal SST variability. We identified high-frequency SST modes in the South Atlantic Ocean using an autoencoder artificial neural network applied to high-pass filtered monthly SST data. The extreme gradient boosting feature importance metric revealed that the different high-frequency SST modes including localized anomaly off the west coast of northern Namibia; SST anomaly in the Atlantic Niño region; widespread anomaly in the subtropical South Atlantic, and a dipole anomaly off the southeast coast of South America and the GoG emerged as the most effective predictors of average SST in the GoG, achieving a correlation of 0.89 and explaining 78% of the variance with a one-month lead time forecast. Concerning the low-frequency modes, our findings show that while the Tropical South Atlantic mode and the South Atlantic Ocean dipole mode predominantly correlate with sub-seasonal SST in the GoG, other tropical and high-latitude modes also significantly influence SST variability in the GoG. Finally, we presented a novel framework for accurate sub-seasonal SST prediction in the GoG using machine learning and further identified low and high-frequency modes that can provide sub-seasonal to inter-decadal predictability of SST in the GoG.</p>

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High and low-frequency climate modes associated with sub-seasonal sea surface temperature variability in the Gulf of Guinea

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月14日

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  • Chibuike Chiedozie Ibebuchi,
  • Omon A. Obarein

摘要

Sea Surface Temperature (SST) variability in the Gulf of Guinea (GoG) significantly influences the regional climate of West Africa and the marine ecosystem of the Atlantic Ocean. This study examines both high-frequency (seasonal to sub-seasonal) and low-frequency (interannual to interdecadal) climatic modes contributing to SST variability in the GoG. Findings indicate that the El Niño Southern Oscillation is associated with SST variability in the GoG. Nonetheless, the Tropical South Atlantic Ocean’s SST patterns are the primary driver of the GoG’s seasonal to sub-seasonal SST variability. We identified high-frequency SST modes in the South Atlantic Ocean using an autoencoder artificial neural network applied to high-pass filtered monthly SST data. The extreme gradient boosting feature importance metric revealed that the different high-frequency SST modes including localized anomaly off the west coast of northern Namibia; SST anomaly in the Atlantic Niño region; widespread anomaly in the subtropical South Atlantic, and a dipole anomaly off the southeast coast of South America and the GoG emerged as the most effective predictors of average SST in the GoG, achieving a correlation of 0.89 and explaining 78% of the variance with a one-month lead time forecast. Concerning the low-frequency modes, our findings show that while the Tropical South Atlantic mode and the South Atlantic Ocean dipole mode predominantly correlate with sub-seasonal SST in the GoG, other tropical and high-latitude modes also significantly influence SST variability in the GoG. Finally, we presented a novel framework for accurate sub-seasonal SST prediction in the GoG using machine learning and further identified low and high-frequency modes that can provide sub-seasonal to inter-decadal predictability of SST in the GoG.