• DOI:10.1002/wcc.527
• Corpus ID: 135095604

Equatorial Atlantic variability—Modes, mechanisms, and global teleconnections

@article{Lbbecke2018EquatorialAV,
  title={Equatorial Atlantic variability—Modes, mechanisms, and global teleconnections},
  author={Joke F. L{\"u}bbecke and Bel{\'e}n Rodr{\'i}guez‐Fonseca and Ingo Richter and Marta Mart{\'i}n‐Rey and Teresa Losada and Irene Polo and Noel S. Keenlyside},
  journal={Wiley Interdisciplinary Reviews: Climate Change},
  year={2018},
  volume={9}
}

• J. Lübbecke, B. Rodríguez‐Fonseca, +4 authors N. Keenlyside
• Published 1 July 2018
• Environmental Science
• Wiley Interdisciplinary Reviews: Climate Change

Sea surface temperature (SST) variability in the tropical Atlantic Ocean strongly impacts the climate on the surrounding continents. On interannual time scales, highest SST variability occurs in the eastern equatorial region and off the coast of southwestern Africa. The pattern of SST variability resembles the Pacific El Niño, but features notable differences, and has been discussed in the context of various climate modes, that is, reoccurring patterns resulting from particular interactions in the climate system. Here, we attempt to reconcile those different definitions, concluding that almost all of them are essentially describing the same mode that we refer to as the “Atlantic Niño.” We give an overview of the mechanisms that have been proposed to underlie this mode, and we discuss its interaction with other climate modes within and outside the tropical Atlantic. The impact of Atlantic Niño‐related SST variability on rainfall, in particular over the Gulf of Guinea and north eastern South America is also described. An important aspect we highlight is that the Atlantic Niño and its teleconnections are not stationary, but subject to multidecadal modulations. Simulating the Atlantic Niño proves a challenge for state‐of‐the‐art climate models, and this may be partly due to the large mean state biases in the region. Potential reasons for these model biases and implications for seasonal prediction are discussed.