The Linkage Between Quasi-Biennial Oscillation and Precipitation in Sub-Saharan Africa

This study investigates the relationship between the Quasi-Biennial Oscillation (QBO) and precipitation anomalies over sub-Saharan Africa (SSA) during the December–February season (DJF season) using ERA5 reanalysis and CHIRPS observations. The ERA5 accurately reproduces CHIRPS precipitation patterns, but their QBO relationships differ: ERA5 shows QBO correlates with the second and third precipitation mode, while CHIRPS exhibits stronger correlation with the first mode. Based on CHIRPS, the net QBO effect identifies southern, central SSA, and southern Madagascar as regions sensitive to precipitation reduction, with significant phase dependence and regional heterogeneity. During the westerly QBO phase, precipitation increases over southern SSA and northern Madagascar but decreases over central SSA. During the easterly phase, drying dominates most of SSA. Southern SSA drying is primarily linked to the easterly phase, while central SSA and southern Madagascar drying is associated with the westerly phase. Diagnostic analysis reveals the westerly phase enhances ascent and moisture convergence over southern SSA, while the easterly phase induces subsidence and moisture divergence. This study reveals the differential impacts and mechanisms of various QBO phases on precipitation over SSA, clarifies the phase dependence of regional responses and their physical processes, and provides a new stratospheric predictor for understanding the driving mechanisms of precipitation variability over extratropical Africa and improving regional precipitation prediction.

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Publication Details

Journal
Remote Sensing
Published
2026-05-11
DOI
https://doi.org/10.3390/rs18101507
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

The Linkage Between Quasi-Biennial Oscillation and Precipitation in Sub-Saharan Africa

Dingzhu Hu, Tanimu Abubakar Sadiq, Yue Huang, Azarias Munyentwari
Remote Sensing
Climate variability and models
article

The Linkage Between Quasi-Biennial Oscillation and Precipitation in Sub-Saharan Africa

Dingzhu Hu, Tanimu Abubakar Sadiq, Yue Huang, Azarias Munyentwari
article en

Abstract

This study investigates the relationship between the Quasi-Biennial Oscillation (QBO) and precipitation anomalies over sub-Saharan Africa (SSA) during the December–February season (DJF season) using ERA5 reanalysis and CHIRPS observations. The ERA5 accurately reproduces CHIRPS precipitation patterns, but their QBO relationships differ: ERA5 shows QBO correlates with the second and third precipitation mode, while CHIRPS exhibits stronger correlation with the first mode. Based on CHIRPS, the net QBO effect identifies southern, central SSA, and southern Madagascar as regions sensitive to precipitation reduction, with significant phase dependence and regional heterogeneity. During the westerly QBO phase, precipitation increases over southern SSA and northern Madagascar but decreases over central SSA. During the easterly phase, drying dominates most of SSA. Southern SSA drying is primarily linked to the easterly phase, while central SSA and southern Madagascar drying is associated with the westerly phase. Diagnostic analysis reveals the westerly phase enhances ascent and moisture convergence over southern SSA, while the easterly phase induces subsidence and moisture divergence. This study reveals the differential impacts and mechanisms of various QBO phases on precipitation over SSA, clarifies the phase dependence of regional responses and their physical processes, and provides a new stratospheric predictor for understanding the driving mechanisms of precipitation variability over extratropical Africa and improving regional precipitation prediction.

Remote SensingVol. 18(10)
Nanjing University of Information Science and Technology (CN), Ministry of Education (BD)
National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province
Openalex Percentile: Top 6%
Climate variability and models
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