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Flore Mounier, Serge Janicot, and George N. Kiladis

1. Introduction The central and West African regions depend heavily on rainfall during the summer monsoon season, which corresponds to the northernmost migration of the intertropical convergence zone (ITCZ; Hastenrath 1991 ). A large amount of work has already been completed on rainfall variability of the African monsoon, through studies on mesoscale convective systems (MCSs; Laing and Fritsch 1993 , 1997 ; Hodges and Thorncroft 1997 ; Mathon and Laurent 2001 ), synoptic-scale easterly

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Jennifer M. Collins

smaller scales. On these scales natural climate variability is relatively larger, making it harder to distinguish changes expected due to external forcings. Uncertainties in local forcings and feedbacks also make it difficult to estimate the contribution of greenhouse gas increases to observed small-scale temperature changes ( Hegerl et al. 2007 ). Africa is unique in that it is the only continent that, almost symmetrically, straddles the equator, and hence experiences such a varied climate with both

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Tsing-Chang Chen, Shih-Yu Wang, and Adam J. Clark

1. Introduction After African easterly waves (AEWs) were identified by Carlson (1969a , b ), Burpee (1974) found that these waves propagate along two distinct tracks—one following the Saharan thermal low and the other in the rainy zone of West Africa. AEWs along the northern and southern tracks are denoted, respectively, as AEW n s and AEW S s. These two tracks of AEWs were later confirmed by post–First Global Atmospheric Research Programme (GARP) Global Experiment (FGGE)] data (e.g., Reed

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Travis J. Elless and Ryan D. Torn

1. Introduction African easterly waves (AEWs) are synoptic-scale features that traverse over sub-Saharan Africa and are often associated with significant rainfall via mesoscale convective systems (e.g., Payne and McGarry 1977 ). Previous observational (e.g., Norquist et al. 1977 ) and idealized modeling (e.g., Thorncroft and Hoskins 1994 ) studies indicate that AEWs primarily grow through both barotropic and baroclinic energy conversions, as well as through convective/diabatic processes (e

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John R. Christy, William B. Norris, and Richard T. McNider

in understanding the precision inherent in such compilations, especially when the time span for documenting changes increases to cover many decades. Gridded global datasets of surface temperature may misrepresent trends in undersampled and poorly observed grid boxes (e.g., Christy et al. 2006 ). Undersampling occurs when observations are scarce or because much useful information has not yet been digitized. This is true of much of the African continent, and in particular of East Africa. Although

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Benjamin I. Cook, Kimberly Slinski, Christa Peters-Lidard, Amy McNally, Kristi Arsenault, and Abheera Hazra

climate and land surface hydrology with direct relevance for agriculture, at least in regions where water is a limiting factor on vegetation growth. One region that may significantly benefit from improved seasonal climate forecasts, especially for hydrological variables like TWS, is Africa. Africa is one of the most food insecure regions in the world ( Clover 2003 ; Thompson et al. 2010 ) and populations across the continent are especially vulnerable to climate extremes, including heat waves

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Rachel A. Stratton, Catherine A. Senior, Simon B. Vosper, Sonja S. Folwell, Ian A. Boutle, Paul D. Earnshaw, Elizabeth Kendon, Adrian P. Lock, Andrew Malcolm, James Manners, Cyril J. Morcrette, Christopher Short, Alison J. Stirling, Christopher M. Taylor, Simon Tucker, Stuart Webster, and Jonathan M. Wilkinson

1. Introduction The Future Climate for Africa (FCFA) Improving Model Processes for African Climate (IMPALA) project aims to deliver a step change in global climate model capability for Africa by delivering reductions in model systematic errors through improved understanding and representation of the drivers of African climate and hence reducing uncertainty in future projections. This ambitious project has chosen to focus on a single model, the Met Office Unified Model (UM), so there is rapid

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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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Gareth Berry, Chris Thorncroft, and Tim Hewson

1. Introduction African easterly waves (AEWs) are westward-propagating synoptic-scale disturbances that exist over Africa and the tropical North Atlantic during boreal summer (e.g., Burpee 1972 ). AEWs are important because they are known to modulate rainfall over Africa (see, e.g., Payne and McGarry 1977 ) and act as precursors to tropical cyclones in both the Atlantic and eastern Pacific Ocean basins (e.g., Avila and Pasch 1992 ; Frank 1970 ). Synoptic studies of AEWs (e.g., Carlson 1969

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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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