Southern African Monthly Rainfall Variability: An Analysis Based on Generalized Linear Models

Abstract

Southern Africa is characterized by a high degree of rainfall variability, affecting agriculture and hydrology, among other sectors. This paper aims to investigate such variability and to identify stable relationships with its potential drivers in the climate system; such relationships may be used as the basis for the statistical downscaling of climate model outputs, for example. The analysis uses generalized linear models (GLMs). The GLMs are fitted to twentieth-century observational data for the period 1957–2006 to characterize the dependence of monthly precipitation occurrences and amounts upon the climate indicators of interest. In contrast with many of the analyses that have previously been used to investigate controls on precipitation in the region, GLMs allow for the investigation of the relationships between different components of the climate system (geographical and climatic drivers) simultaneously. Six climate factors were found to drive part of the rainfall variability in the region, and their modeled effect upon rainfall occurrences and amounts resulted in general agreement with previous studies. Among the retained indices, relative humidity and El Niño accounted for the highest degree of explained variability. The location and intensity of the jet stream were also found to have a statistically significant and physically meaningful effect upon rainfall variability.