A review of African easterly waves: formation, growth, structure, and evolution

Núñez Ocasio, K. M., Lawton, Q. A., Brammer, A., Bercos-Hickey, E., Bourdin, S., Feng, X. ORCID: https://orcid.org/0000-0003-4143-107X and Schwendike, J. (2026) A review of African easterly waves: formation, growth, structure, and evolution. Journal of the Atmospheric Sciences, 83 (6). pp. 901-926. ISSN 1520-0469 doi: 10.1175/JAS-D-25-0175.1

Abstract/Summary

This paper presents a comprehensive review of African Easterly Waves (AEWs), which are cold-core, synoptic-scale disturbances that exhibit horizontal and vertical tilt, often westward with height, consistent with their baroclinic–barotropic nature. AEWs typically have wavelengths of 2,000–6,000 km, periods of 3–5 days, and westward propagation speeds of 7–9 m s{-1}. Although AEWs have been observed and linked to African rainfall variability and tropical cyclone (TC) precursors since the 1930s, the theoretical framework explaining their formation was developed over 50 years ago and continues to inform our understanding of their energetics and evolution. The initiation and the horizontal and vertical structure of AEWs are reviewed, as these characteristics, especially their convective nature, can indicate wave growth and, in some cases, TC genesis. While TC genesis is not the focus of this paper, it emerges in AEW evolution. Perspectives differ on whether African Easterly Jet (AEJ) instabilities or upscale convective forcing are more critical to AEW growth, though both views have been shaped by available data and modeling tools. We argue the mechanisms are complementary. We include a summary of AEW tracking methods to assist future researchers in selecting approaches. Notably, most trackers tend to be more sensitive to southern-track AEWs than northern-track AEWs, likely due to stronger convective signals in the former systems. Finally, we discuss how AEW activity may respond to climate change, with evidence of a strengthening and poleward shift since 1980. The advent of convection-permitting models and machine learning tools offers potential for new insights into AEW growth and evolution.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/129398
Identification Number/DOI	10.1175/JAS-D-25-0175.1
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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