15B.1
Spatiotemporal Variability of Precipitation over Africa

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Thursday, 10 January 2013: 3:30 PM
Spatiotemporal Variability of Precipitation over Africa
Ballroom C (Austin Convention Center)
Hamada S. Badr, Johns Hopkins University, Baltimore, MD; and B. F. Zaitchik and A. K. Dezfuli

Poster PDF (6.7 MB)

Africa is characterized by considerable spatiotemporal variability of precipitation. The associated extreme events such as droughts and floods have serious impacts on society, environment and economy. African precipitation variability is determined by a wide range of factors, including prevailing patterns of sea surface temperature, atmospheric winds, regional climate fluctuations in the Indian and Atlantic Oceans, and the El Niño Southern Oscillation (ENSO) phenomenon. ENSO brings above average rainfall to some regions and reduced rainfall to others. Climatologically, the equatorial belt generally has high rainfall, whereas northern and southern African countries and those in the Horn of Africa are typically arid or semi-arid. All parts of Africa, even those that typically have high rainfall, experience climatic variability and hydrometeorological extremes. There is also some evidence that natural disasters have increased in frequency and severity, particularly drought in the Sahel. This study is focused on identifying regional patterns of inter-annual precipitation variability over Africa based on monthly precipitation estimates from meteorological stations, reanalyses, and climate models. To assess the spatiotemporal patterns of precipitation, principal component analysis and hierarchical clustering were applied to each dataset. It was found that the spatial patterns of precipitation over Africa vary over the course of the year. This led us to define characteristic “seasons” for selected regions as different sets of months, in order to allow for coherent classification of precipitation variability in each region. These characteristic seasons were then used to develop regionally and seasonally specific statistical predictive precipitation models. In addition, the sensitivity of regionalization and statistical models to choice of precipitation data source was assessed, and implications for the evaluation of climate models in Africa will be discussed.

Supplementary URL: http://blaustein.eps.jhu.edu/~zaitchik