An investigation into the role of synoptic conditions on Central African precipitation variability
Komkoua Mbienda, A. J.
Pokam Mba, Wilfried
Vondou, Derbetini Appolinaire
Mishra, Saroj Kanta
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Embargo End Date2023-03-18
Permanent link to this recordhttp://hdl.handle.net/10754/676349
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AbstractThe synoptic precipitation variability over Central Africa (CA) in the March-to-May (MAM) and September-to-November (SON) seasons is investigated in this study. The composite analysis is used to highlight the evolution of synoptic precipitation, related convection, and dynamic fields. Composite analysis findings show that synoptic precipitation anomalies increase/fade with an increase/decrease in convection, with the largest amplitude over 5° S–10° N and 2.5–20° E in MAM and 5° N–7.5° N and 5–15° E in SON. Precipitation anomalies and convection associated with the synoptic variability progress eastward preceded (followed) by eastward (westward) low-level wind. This synoptic sequence is associated with a downward motion and an inflow, indicating the storm’s presence, which contributes significantly to precipitation initiation over CA. The empirical orthogonal function for precipitation during the SON season is lower than that observed during the MAM season in the context of the size and spatial pattern. During these precipitation occurrences, the zonal moisture component contributes the most to the moisture initiation over the region. The findings of this study show that the observed convergence/divergence position remains unchanged in intensity irrespective of the positive/negative precipitation events. Overall, the synoptic-scale conditions in MAM contribute around 58% of the total variability in precipitation.
CitationZebaze, S., Pathak, R., Komkoua Mbienda, A. J., Pokam Mba, W., Salunke, P., Anand, A., Vondou, D. A., Lenouo, A., & Mishra, S. K. (2022). An investigation into the role of synoptic conditions on Central African precipitation variability. Acta Geophysica. https://doi.org/10.1007/s11600-022-00754-2
SponsorsThe authors would like to thank NOAA ( http://www.cdc.noaa.gov ) for GPCP, OLR and NCEP datasets. This research work is partly supported by the DST CoE in Climate Modeling. The authors are thankful to the ECMWF for providing Era-Interim products available. The authors are also grateful to the two anonymous reviewers for their constructive and helpful suggestions which contributed to improve the manuscript.
PublisherSpringer Science and Business Media LLC