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The Predictability of Rainfall over the Greater Horn of Africa. Part II: Prediction of Monthly Rainfall during the Long Rains

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  • 1 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida
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Abstract

Seasonal prediction of the boreal spring rains in the Greater Horn of Africa has been notoriously challenging. Predictability is markedly lower than during the autumnal rainy season. Part I of this article explored predictability at the seasonal scale, using multiple linear regression. However, the three months of the boreal spring season are clearly different climatologically and with respect to the prevailing atmospheric circulation and controls on interannual variability. For that reason, the current study follows up on Part I by examining the predictability of the three months individually. The current study utilizes 1- and 2-month lead times and the results are evaluated via cross validation. This approach provided improved skill for April and May in the equatorial rainfall region, but not for March in this region and not for the region with predominantly summer rainfall. Overall, the best predictors are shown to be atmospheric variables, most often zonal and meridional wind. Sea surface temperatures and sea level pressure provided little predictive skill.

Corresponding author address: Sharon E. Nicholson, Dept. of Earth, Ocean and Atmospheric Science, Florida State University, P.O. Box 3064520, Tallahassee, FL 32306-4520. E-mail: snicholson@fsu.edu

Abstract

Seasonal prediction of the boreal spring rains in the Greater Horn of Africa has been notoriously challenging. Predictability is markedly lower than during the autumnal rainy season. Part I of this article explored predictability at the seasonal scale, using multiple linear regression. However, the three months of the boreal spring season are clearly different climatologically and with respect to the prevailing atmospheric circulation and controls on interannual variability. For that reason, the current study follows up on Part I by examining the predictability of the three months individually. The current study utilizes 1- and 2-month lead times and the results are evaluated via cross validation. This approach provided improved skill for April and May in the equatorial rainfall region, but not for March in this region and not for the region with predominantly summer rainfall. Overall, the best predictors are shown to be atmospheric variables, most often zonal and meridional wind. Sea surface temperatures and sea level pressure provided little predictive skill.

Corresponding author address: Sharon E. Nicholson, Dept. of Earth, Ocean and Atmospheric Science, Florida State University, P.O. Box 3064520, Tallahassee, FL 32306-4520. E-mail: snicholson@fsu.edu
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