Deriving Useful Information from Bimonthly Global-Scale Climate Analysis for Climate Change Adaptation Over East Africa

Abstract

Implementation of appropriate climate change adaptation strategies is contingent on a good understanding of climate variability. Efforts to adapt to climate change impacts in East African societies have flourished. However, an area of research which has been neglected and could enhance adaptive capacity is bimonthly global-scale climate analysis in relationship to the long rains, during the climatologically prominent phase of El Niño Southern Oscillation (ENSO). Empirical analyses were carried out using nearly 60 years of standardized gridded rainfall, horizontal wind and sea surface temperature (SST) data, to gain predictive understanding of the region’s climate. This study has delineated SST and divergent circulation features related to three of the four rainfall modes. The modes responded differently to the Pacific ENSO, Atlantic and Indian Oceans. However, there was no clear relationship between the second mode and the global SST distributions. Having substantiated this with monthly and seasonal-scale SST analyses, it suggested that this atypical pattern warranted numerical modeling studies or should be verified using other high resolution datasets. The SST predictor features identified may be used to enhance operational seasonal climate prediction scheme. In this way, end users would be better prepared to select appropriate climate change adaption options.