Climate Dynamics

, Volume 49, Issue 5–6, pp 2161–2178 | Cite as

A climatology of potential severe convective environments across South Africa

  • R. C. BlameyEmail author
  • C. Middleton
  • C. Lennard
  • C. J. C. Reason


Severe thunderstorms pose a considerable risk to society and the economy of South Africa during the austral summer months (October–March). Yet, the frequency and distribution of such severe storms is poorly understood, which partly stems out of an inadequate observation network. Given the lack of observations, alternative methods have focused on the relationship between severe storms and their associated environments. One such approach is to use a combination of covariant discriminants, derived from gridded datasets, as a probabilistic proxy for the development of severe storms. These covariates describe some key ingredient for severe convective storm development, such as the presence of instability. Using a combination of convective available potential energy and deep-layer vertical shear from Climate Forecast System Reanalysis, this study establishes a climatology of potential severe convective environments across South Africa for the period 1979–2010. Results indicate that early austral summer months are most likely associated with conditions that are conducive to the development of severe storms over the interior of South Africa. The east coast of the country is a hotspot for potential severe convective environments throughout the summer months. This is likely due to the close proximity of the Agulhas Current, which produces high latent heat fluxes and acts as a key moisture source. No obvious relationship is established between the frequency of potential severe convective environments and the main large-scale modes of variability in the Southern Hemisphere, such as ENSO. This implies that several factors, possibly more localised, may modulate the spatial and temporal frequency of severe thunderstorms across the region.


Convective environments South Africa CFSR reanalysis Inter-annual variability ENSO 



The CFSR data was obtained from Research Data Archive (RDA), managed by the Data Support Section (DSS) of the Computational and Information Systems Laboratory (CISL) at NCAR ( The Nino 3.4 index data was obtained from the Climate Prediction Center ( The authors thank Alex Shabala for assistance with some of the computation. We are also grateful for the contribution of the two reviewers who helped improve the manuscript. This work was supported by the Natural Environment Research Council (NERC) Future Climate For Africa (FCFA) regional consortium project ‘UMFULA’ and the South African National Research Foundation (NRF).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Climate System Analysis GroupUniversity of Cape TownRondeboschSouth Africa
  2. 2.Department of OceanographyUniversity of Cape TownRondeboschSouth Africa

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