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Lara Peck
and
David William Hedding

Sacramento, California. Van Schalkwyk and Dyson (2013) have developed a fog-type classification method that classifies fog events at Cape Town International Airport (CTIA), in Cape Town, South Africa, according to their primary formation mechanism. This fog-type classification method aims to assist aviation forecasters with a detailed description of the types of fog, their characteristics, and associated synoptic circulation patterns to limit the impacts of fog at CTIA. The operational response to a

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E. R. M. Archer van Garderen

1. Introduction Farming with domesticated livestock has long been a feature of livelihoods on the African continent. In southern Africa, archaeological evidence shows, for example, signs of cattle herding in rock paintings (see Manhire et al. 1986 , and others). The Nguni people of eastern southern Africa (the Seswati, Zulu, and Xhosa people) may have grazed with domestic livestock for more than 10 000 yr ( Palmer and Ainslie 2010 ); however, it is also argued that a more likely date for the

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Warren J. Tennant
,
Zoltan Toth
, and
Kevin J. Rae

1. Introduction Weather forecasts have potential use at a variety of space and time scales. As a public weather forecast service, the South African Weather Service (SAWS) is tasked to provide a comprehensive forecast service from a few hours ahead through all scales up to several seasons ahead. The medium range (3–14 days) is particularly popular through a number of sectors and thus considerable effort has been invested in improving forecasts for this time scale. To this end the National

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Hector Chikoore
and
Mark R. Jury

1. Introduction While there has been significant progress in understanding climate variability and predictability in many regions of Africa, the role of vegetation in the African climate system is less established. Whereas vegetation growth and distribution are largely determined by climate ( Woodward 1987 ; Wang 2004 ), vegetation and land-use characteristics can feed back on the climate ( Zeng and Neelin 2000 ; Wang 2004 ). The impact of vegetation variability has received

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Alice M. Grimm
and
Chris J. C. Reason

1. Introduction Significant relationships exist between the Atlantic Ocean and climate variability in South America and Africa ( Nobre et al. 2006 ; Reason et al. 2006 ; Grimm and Zilli 2009 ). However, teleconnections between South American and African climate are poorly understood. Through AGCM simulations, K. Cook et al. (2004) concluded that South America and Africa influence each other’s climate and suggested a stronger influence of Africa on South America. The influence of South

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Coleen Vogel
,
Ingrid Koch
, and
Koos Van Zyl

1. Introduction Africa’s vulnerability to climate variability and climate change (CC), including periods of severe drought, is generally acknowledged by natural resource managers, farmers, scientists, and policy makers ( Boko et al. 2007 ). Drought severely curtails African economies [gross domestic product (GDP) losses], but it also erodes livelihoods and coping capacities ( Benson and Clay 1994 ; de Waal and Whiteside 2003 ). Globally, areas affected by droughts are likely to increase

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B. Pohl
,
Y. Richard
, and
N. Fauchereau

days, but more generally varies from 30 to 60 days. Therefore, it holds out the promise of significant medium-range predictability in the tropical atmosphere ( Waliser et al. 1999 ; Waliser et al. 2003 ). The MJO was shown to strongly interact with the Indian ( Yasunari 1979 , 1980 , 1981 ) and Australian ( Hendon and Liebmann 1990a , b ) monsoon systems. Over Africa the effects of the intraseasonal oscillation are not as well understood. Matthews (2004) showed that it significantly affects

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Sithabile Hlahla
,
Mulala Danny Simatele
,
Trevor Hill
, and
Tafadzwanashe Mabhaudhi

1. Introduction The climate of South Africa is changing, with frequent episodes of droughts, flash flooding, hailstorms, and extreme heat events. The country’s mean annual temperatures have increased by more than 1.5 times, with the observed global average of 0.65°C reported by the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) for the period from 1950 [South Africa’s National Department of Environmental Affairs ( DEA) 2013 ]. Moreover, maximum and

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Rondrotiana Barimalala
,
Ross C. Blamey
,
Fabien Desbiolles
, and
Chris J. C. Reason

1. Introduction Climate variability over southern Africa is driven by a complex interaction between large-scale climate forcings such as El Niño–Southern Oscillation (ENSO), regional sea surface temperature (SST) patterns, and regional atmospheric circulation systems [e.g., the Angola low (AL) and Botswana high (BH)] at various temporal scales ( Lindesay 1988 ; Nicholson and Kim 1997 ; Reason et al. 2000 ; Behera and Yamagata 2001 ; Reason and Rouault 2002 ; Reason 2001 , 2016 ; Blamey

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Robert T. Maisha
,
Thando Ndarana
,
Francois A. Engelbrecht
,
Marcus Thatcher
,
Mary-Jane M. Bopape
,
Jacobus van der Merwe
,
Yerdashin Padayachi
, and
Cecilia Masemola

South African cities. Therefore, it is important to study UHIs and the different mechanisms responsible for their formation using both observations and high-resolution atmospheric models set up with urban-scale features ( Wang and Li 2017 ; Garuma 2018 ). Urban climate models (UCMs) are coupled to regional atmospheric models to investigate the dynamics of the urban climate resulting from global emission scenarios and could be used for urban planning ( Thatcher and Hurley 2012 ). Initially, UCMs

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