Author:
Ouala Said,Chapron Bertrand,Collard Fabrice,Gaultier Lucile,Fablet Ronan
Abstract
AbstractSea surface temperature (SST) is a critical factor in the global climate system and plays a key role in many marine processes. Understanding the variability of SST is therefore important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management. In this study, we use machine learning techniques to analyze SST anomaly (SSTA) data from the Mediterranean Sea over a period of 33 years. The objective is to best explain the temporal variability of the SSTA extremes. These extremes are revealed to be well explained through a non-linear interaction between multi-scale processes. The results contribute to better unveil factors influencing SSTA extremes, and the development of more accurate prediction models.
Publisher
Springer Nature Switzerland
Reference26 articles.
1. Grant R Bigg, TD Jickells, PS Liss, and TJ Osborn. The role of the oceans in climate. International Journal of Climatology: A journal of the Royal Meteorological Society, 23(10):1127–1159, 2003.
2. Song Yang, Zhenning Li, Jin-Yi Yu, Xiaoming Hu, Wenjie Dong, and Shan He. El niño–southern oscillation and its impact in the changing climate. National Science Review, 5(6):840–857, 2018.
3. Gerold Siedler, John Gould, and John Church. Ocean circulation and climate: observing and modelling the global ocean. Elsevier, 2001.
4. Alexander Otto, Friederike EL Otto, Olivier Boucher, John Church, Gabi Hegerl, Piers M Forster, Nathan P Gillett, Jonathan Gregory, Gregory C Johnson, Reto Knutti, et al. Energy budget constraints on climate response. Nature Geoscience, 6(6):415–416, 2013.
5. Kevin E Trenberth and John T Fasullo. An apparent hiatus in global warming? Earth’s Future, 1(1):19–32, 2013.