AIMS Environmental Science (Jun 2018)

Circulation mechanisms responsible for wet or dry summers over Zimbabwe

  • Moven Manjowe,
  • Terence Darlington Mushore,
  • Juliet Gwenzi,
  • Collen Mutasa,
  • Electdom Matandirotya,
  • Emmanuel Mashonjowa

DOI
https://doi.org/10.3934/environsci.2018.3.154
Journal volume & issue
Vol. 5, no. 3
pp. 154 – 172

Abstract

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Climate change has resulted in increased rainfall variability over many parts of the world including Southern Africa. As such, droughts and floods have become a frequent phenomenon in Zimbabwe and have potential to intensify socio-economic stressors. This study examined possible forcing factors behind the occurrence of extreme summer events using re-analysis datasets. Composite analysis and correlation methods were used to identify circulation mechanisms and their strength in determining rainfall patterns in Zimbabwe. Predominantly northerly airflow in the lower troposphere was found to favor wet while southerly airflow favors dry seasons. Negative geopotential anomalies (minimum of −20 hPa) to the west of Zimbabwe in the middle levels characterize wet summers which swing to positive anomalies (+24 hPa) during dry summers. Positive SST anomalies (maximum of 0.4) exist to the southwest of Madagascar extending to the western shore on the Angola-Namibian border characterize wet summers which swing to negative anomalies (−0.2 ºC) during dry summer seasons. SST anomalies in the South western Indian and South eastern Atlantic oceans are crucial in the determination of the strength of both the South Indian and Atlantic Ocean high pressure systems which in turn control moisture advection and convergence into Zimbabwe during the summer period. If these SST anomalies at lag times of about 3 months can be used to predict the incoming summer circulation patterns then the accuracy of summer seasonal outlook forecasts can be improved. Studying the mechanisms behind drought and flood occurrence is important to the country which is in the process of downscaling regional prediction products to improve the accuracy of seasonal forecasts. These findings are useful in crafting relevant measures to maximize the benefits and minimize the risks of extreme rainfall events.

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