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Climate Dynamics

, Volume 44, Issue 5–6, pp 1595–1609 | Cite as

Drought regimes in Southern Africa and how well GCMs simulate them

  • Eva L. Ujeneza
  • Babatunde J. AbiodunEmail author
Article

Abstract

This paper presents the spatial and temporal structures of drought regimes in Southern Africa and evaluates the capability of ten global climate models (GCMs) in simulating the regimes. The study uses a multi-scaled standardized index (called standardized precipitation evapo-transpiration index, SPEI) in characterizing droughts over Southern Africa at 3- and 12-month scales. The spatial patterns of the drought regimes are identified using the rotated principal component analysis (PCA) on the SPEI, while the temporal characteristics of the drought regimes are studied using wavelet analysis. The relationship between each drought regime and global SSTs (and climate indices) is quantified using correlation analysis and wavelet coherence analysis. The study also quantifies the capability of the GCMs in simulating the drought regimes. The PCA results show four main drought regimes that jointly explain about 50 % SPEI variance over South Africa. The drought regimes (hereafter PF1, PF2, PF3 and PF4) centre over the south-western part of Southern Africa (i.e. South Africa, Botswana and Namibia common border), Zimbabwe, Tanzania, and Angola, respectively. PF1, PF2 and PF4 are strongly correlated with SST over the South Atlantic, Tropical Pacific and Indian Oceans, while PF3 is strongly correlated with the SST over the Tropical Pacific, Atlantic and Indian Oceans. The drought regimes (except PF4) have significant coherence with some atmospheric teleconnection, but the strength, duration, and phase of the coherence vary with time. All the GCMs simulate the drought regimes better at a 3-month scale than at a 12-month scale. At a 3-month scale, 70 % of the GCMs simulate all the drought regimes with a high correlation coefficient (r > 0.6), but at a 12-month scale only 60 % of the models simulate at least three of the drought regimes with a high correlation coefficient (r > 0.6). The results of this study have applications in using GCMs to study the underlying atmospheric dynamics that control droughts and to understand the impacts of global warming on droughts.

Keywords

Droughts Southern Africa Climate indices Global climate models 

Notes

Acknowledgments

The project was supported with grants from the Water Research Commission (WRC, South Africa) and the National Research Foundation (NRF, South Africa). Computations facility was provided by the Centre for High Performance Computing (CHPC, South Africa). We thank the two anonymous reviewers, whose comments improved the quality of this manuscript.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Climate Systems Analysis Group, Department of Environmental and Geographical ScienceUniversity of Cape TownCape TownSouth Africa

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