Evolving Paradigms of Climatic Processes and Atmospheric Circulation Affecting Africa
Summary and Keywords
Classic paradigms describing meteorological phenomena and climate have changed dramatically over the last half-century. This is particularly true for the continent of Africa. Our understanding of its climate is today very different from that which prevailed as recently as the 1960s or 1970s. This article traces the development of relevant paradigms in five broad areas: climate and climate classification, tropical atmospheric circulation, tropical rain-bearing systems, climatic variability and change, and land surface processes and climate. One example is the definition of climate. Originally viewed as simple statistical averages, it is now recognized as an environmental variable with global linkages, multiple timescales of variability, and strong controls via earth surface processes. As a result of numerous field experiments, our understanding of tropical rainfall has morphed from the belief in the domination by local thunderstorms to recognition of vast systems on regional to global scales. Our understanding of the interrelationships with land surface processes has also changed markedly. The simple Charney hypothesis concerning albedo change and the related concept of desertification have given way to a broader view of land–atmosphere interaction. In summary, there has been a major evolution in the way we understand climate, climatic variability, tropical rainfall regimes and rain-bearing systems, and potential human impacts on African climate. Each of these areas has evolved in complexity and understanding, a result of an explosive growth in research and the availability of such investigative tools as satellites, computers, and numerical models.
Over the last half-century ideas about meteorology and climate have changed dramatically, particularly for the tropics. Because Africa includes the largest continental area in the tropics, the prevailing understanding of its climate as of the early 21st century is very different from that which prevailed as recently as the 1960s or 1970s. This article traces the development of relevant paradigms in five broad areas: climate and climate classification, tropical circulation, tropical rain-bearing systems, climatic variability and change, and land surface processes and climate. One of the most notable changes is in the definition of climate. Originally viewed as a static, statistical compilation of local weather elements, it is now recognized as an environmental variable with global linkages, multiple timescales of variability, and strong controls via earth surface processes. Similarly, the understanding of tropical rainfall has morphed from the belief in the domination by local thunderstorms to recognition of vast systems on regional to global scales. The understanding of the interrelationships with land surface processes has also changed markedly, with the Charney hypothesis concerning albedo change giving way to a broader view of land–atmosphere interaction. In summary, there has been a major evolution in the conceptualization of climate, climatic variability, tropical rainfall regimes and rain-bearing systems, and potential human impacts on African climate.
Each section of this article will begin with the concept as it prevailed some half a century to a century ago. Climate was defined simply as a temporal average of various statistics. The Intertropical Convergence Zone was considered to be the major control on tropical rainfall, providing uplift and instability for the development of localized thunderstorm. It was also generally indicated as a prevailing factor in interannual variability. Simple scenarios of anthropogenic desertification and deforestation were suggested as factors in the development of drought over Africa. Each area has evolved in complexity and understanding, a result of explosive growth in research and the availability of such investigative tools as satellites, computers, and numerical models.
Climate and Climate Classification
Definition of Climate
The traditional definition of climate is the “average state of the atmosphere for a given timescale” (Houghton, 2002). In many historical sources for Africa, climate is qualitatively described, often on the basis of very limited observation periods. Climatic descriptions abound in explorers’ reports and geographical journals from the 19th century. This was the age of European exploration, and detailed climate summaries were published by the French, Germans, British, Portuguese, and Belgians in particular. Some of the more notable summaries of African climate are those of Borius (1875, 1880), Ginestous (1903), Raulin (1876, 1882), Fitzner (1907), and Supan (1898).
The period over which “climate” is averaged can range from several years to decades or longer. The definition of climate “normal” was standardized by the World Meteorological Organization (WMO) around 1935. Member countries were mandated to produce 30-year averages of weather elements for the period 1901 to 1930. WMO also requested a recomputation of “normal” at the end of every decade to update the statistics (Arguez & Vose, 2011). While such a definition provides a standard for intercomparison of data sets and parameters, it downplays interannual and interdecadal variability. Hence, it provides a false sense of “security” concerning climate conditions. A case in point is the Sahel, where 30-year means can vary by a factor of two.
Africa presents special problems in quantifying climate. The station network changes markedly over time, individual records include many missing years, and in some regions decadal scale variability is tremendous. In this case, one might argue that the best picture of the “normal” at an individual station is the average of all available records. With the Sahel as an exception, 30-year means generally differ by less than 5% (Nicholson, Kim, & Hoopingarner, 1988), and longer periods provide even more stable estimates. Hundreds of station records exist that cover periods of at least 50 years, so that the so-calculated means are reliable. However, they are not suitable when comparisons are made with meteorological variables or other stations. In such cases, standard computation periods are required.
Another component of the definition of climate that has evolved over time is that of variability. In much of the 19th century, climate was assumed to be relatively invariant, at least on historical timescales. Of course, short-term variations such as wet and dry years were always recognized, but there was little expectation that long-term changes of the average conditions would occur over the typical human life span.
One of the first cases of serious climate change being recognized was in southern Africa. There was a fear that the entire country had been drying up over a period of decades. Desiccation of lakes and rivers, for example, bore testimony to this. The concern was so great that a commission to study the problem was established and ultimately recommended flooding the Kalahari to form Lake Kalahari in order to bring back the good rains. In reality, such a desiccation did occur, but much earlier, and it affected much of Africa in the 1820s and 1830s (Nicholson, Klotter, & Dezfuli, 2012). However, the good rains did come back naturally within a few decades.
To account for this, the calculated statistics of mean climate often include some measure of variability. Most common is the standard deviation, the square root of the squared departures from the calculated mean. This value is less stable than the mean itself and is markedly influenced by the extremes of observations, e.g., an extremely wet month or year. For that reason, at least 30 years should be used in its calculation.
Defining Climate Within the Context of the Biogeophysical System Earth
Five components of the earth system have long been recognized—five biogeophysical “spheres” (Figure 1). These include, in addition to the atmosphere, the hydrosphere, lithosphere, biosphere, and cryosphere. The hydrosphere is liquid water (except for that residing in the atmosphere): lakes, rivers, the ocean, and soil moisture. The lithosphere is solid earth, such as soils and the earth’s core and mantle. The biosphere includes all forms of living matter. The cryosphere is water in its solid form: snow, ice, glaciers.
It has long been recognized that factors residing in these spheres can impact climate and its variability (Kutzbach, 1976; Shukla, 1981). Even Alexander von Humboldt (1845) recognized this. These factors act on different timescales (Figure 2), so that climate is the end result of all factors acting collectively. Hence, when a long climatic time series is examined, numerous periodicities on different timescales are evident. Throughout most of Africa the dominant timescales in precipitation (Figure 3) are roughly 2.3, 3.5, and 5 to 6 years (Nicholson & Entekhabi, 1986). The first is a biennial oscillation evident in many climate time series. The remaining peaks demonstrate the influence of El Niño and the Southern Oscillation (Trenberth, 1976) and their influence on the tropical Atlantic and Indian Oceans (Nicholson, Leposo, & Grist, 2001). In the Sahel, a much longer timescale of roughly 30 years is evident, which appears to be associated with ocean influences, namely the Atlantic Multidecadal Oscillation (Zhang & Delworth, 2006).
Recognizing the full importance of these factors and the variability they create, Nicholson (2011) proposed a new and more encompassing definition of climate: the mean weather conditions, their variability, causes, and interrelationships with the global earth system. This is consistent with the paradigm of climate being an integral part of the biogeophysical system earth, with interactions among the five component spheres. In particular, this paradigm recognizes the very important role played by the earth’s surface, including land, in regulating climate and its variability.
The paradigm used to categorize the world’s vast array of climates is termed climate classification. The first attempts at climate classification trace back to the ancient Greeks and were logic based. Three principal climate regions were identified: the frigid zone, the temperate zone, and the torrid zone. These prevailed until the development of weather instrumentation allowed for more detailed climate divisions.
The first classification was based on the distribution of vegetation. The rationale was that climate is a major influence on vegetation, the prevailing patterns of which were better observed that those of weather elements. The parameters defining climatic divisions were adjusted to fit the broad classes of vegetation. The first notable such system was that of Koeppen (later modified by Geiger). He identified five main climatic groups based primarily on thermal conditions: tropical, arid, mid-latitude mild (mesothermal), mid-latitude cold (microthermal), and polar (Koeppen, 1918). As with the original Greek classification, climate was seen as primarily a function of latitude.
An alternative approach was proposed by Bergeron. Termed a genetic classification, it was designed to focus on the factors forcing climate. In effect, however, it was based on weather types. Trewartha and Horn (1980) likewise developed a classification scheme largely based on forcing factors, i.e., the prevailing general circulation features. As an example, the subtropical climates are those where the subtropical highs are dominant in summer and the mid-latitude westerlies in winter. In contrast, Strahler (1978) and Thornthwaite (1933) developed classification schemes based on moisture availability. In these cases, the distinctions among various climate types (e.g., wet or dry) were arbitrary.
The aforementioned classification schemes did not assign any quantitative values to climate. Both Thornthwaite (1948) and Budyko (1986) derived indices that assigned quantitative values to climates, mainly to distinguish among degrees of aridity. Thornthwaite’s moisture index essentially summed the differences between potential evapotranspiration and moisture available from precipitation or soil storage. Budyko’s index, the radiative index of dryness, was energy based and compared the energy available for evaporation (the net radiation) with the amount of energy needed to evaporate the annual average precipitation. Budyko’s index clearly distinguishes among the varying degrees of aridity in the world’s drylands climates, and it shows a strong association with prevailing vegetation types.
Overall, however, within climatology the emphasis on classification has fallen out of favor. For one, it is recognized that the defining characteristics change dramatically from year to year and from decade to decade. Also, with the exception of Budyko’s index, physical interpretation is not straightforward. Finally, it is increasingly recognized that the overall climate character is strongly influenced not only by the annual averages used in the various classification schemes but also by the temporal and spatial partitioning of the various climatic elements. This last point is particularly relevant for Africa.
Atmospheric Circulation over Tropical Africa
It has long been recognized that, meteorologically speaking, the tropics are much different from the rest of the world. The primary contrasts are the absence of Coriolis force as a major factor in atmospheric circulation and the lack of seasonal contrast. This is particularly true for equatorial climates, i.e., those within about 10 degrees of the equator. Our understanding of the atmospheric dynamics governing the tropical atmosphere has been comparatively late to evolve compared to our understanding of mid-latitude dynamics. The topics to be considered here include equatorial circulation, the Intertropical Convergence Zone (ITCZ), the West African monsoon, and wave disturbances.
For the mid-latitudes a long-standing meteorological paradigm is that of the mid-latitude cyclone developed by the Bergen school of meteorology (Jewell, 1981). Associated with the cyclone are frontal zones producing various active weather and separating areas of contrasting winds and thermal conditions. This paradigm could not be applied in the equatorial latitudes because it required the presence of strong spatial variations in temperature and significant Coriolis force, both of which are lacking in the equatorial latitudes.
For these latitudes other paradigms were developed to explain the preferential locations of precipitation. All relied on the assumption that localized convection was the primary source of rainfall in the low latitudes. These include the Walker circulation, the equatorial duct-and-bridge model, and the contrasting impact of easterly vs. westerly winds.
The original paradigm for the Walker circulation was an east-west–oriented cell of vertical motion in the atmosphere above the equatorial Pacific. As first described by Sir Gilbert Walker (1924), it consisted of rising motion in the west and sinking motion in the east, westerlies near the surface and easterlies aloft. Walker documented changes in the intensity of this cell that he termed the “Southern Oscillation.”
The Walker Circulation paradigm evolved to include a global circulation, with additional cells over the Atlantic and Indian Oceans (Bjerknes, 1969). Some early depictions of the Walker Circulation also include vertical cells over Africa and South America (Flohn, 1971; Holton, 2004; Newell, 1979) (Figure 4). The African cell is roughly depicted as rising motion over eastern equatorial Africa and sinking motion over western equatorial Africa. Hastenrath (2000) argues that the African cell exists only in the boreal autumn. Moreover, its intensity changes over time (Nicholson, 2015) (Figure 5). These changes are particularly important in explaining the interannual variability of precipitation over equatorial Africa.
The Walker circulation is an average situation prevalent on timescales of months or longer. It simply creates an environment in which the vertical motion is conducive to or inhibiting the development of precipitation. In an attempt to understand the day-to-day variation in East Africa. Johnson and Mörth (1962) proposed a model based on the distribution of pressure about the equator. The “duct” is the case of low pressure over the equator flanked by high pressure on either side, and the inverse situation (high flanked by low-pressure) was termed the “bridge” pattern. The third component of the model, termed the “drift” pattern, was high pressure on one side of the equator and low pressure on the other.
These pressure patterns prescribe wind patterns, including areas of easterlies and westerlies near the equator. The link to rainfall is a theoretical one that suggests that westerly winds near the equator are more conducive to rising motion and precipitation than are easterly winds (Lettau, 1956). Flohn (1959) cited a study of winds over the equatorial Atlantic that found westerlies were accompanied by precipitation about 25% of the time, while precipitation occurred only 8% of the time when easterly trades prevailed. This paradigm is now seldom considered, and the original statistics may have reflected other factors, such as the impact of the winds on upwelling.
While the Walker cells are clearly a major feature related to equatorial dynamics, the paradigm has expanded to include propagating regions of convection/rainfall associated with the Madden-Julian Oscillation (MJO) or equatorial waves (Serra, Kiladis, & Cronin, 2008). The MJO (Figure 6) is a planetary-scale phenomenon that consists of broad regions of both enhanced and suppressed convection (Wheeler & Hendon, 2004; Zhang, 2006, 2013). This concept originated with the work of Madden and Julian (1994), who showed an intensification of convection over the Indian Ocean and zonal wind changes in the Pacific roughly every 40 to 50 days. Subsequent work on this phenomenon have suggested a broader range of periodicities, so that the MJO is often referred to as the 30- to 60-day or 40- to 50-day oscillation. The MJO propagates eastward near the equator, producing cycles of convection through the tropics.
Madden and Julian (1972, 1994) also showed that the region of enhanced convection in the MJO spawns additional waves of convective activity, termed Kelvin waves and equatorial Rossby waves. The former typically propagate eastward with a period of 3 to 10 days and a wavelength of 3,000 to 7,000 km (Wheeler & Kiladis, 1999). The Rossby waves, in contrast, propagate westward on timescales of 10 to 30 days (Janicot, Mounier, Gervois, Sultan, & Kiladis, 2010; Serra et al., 2014). Although these waves can be triggered by the MJO, they can also arise independently. In such cases, they can also modulate the MJO.
An understanding of the MJO and Kelvin waves is important in the context of precipitation over Africa. Kelvin waves enhance easterly wave activity over West Africa (Mekonnen, Thorncroft, Aiyyer, & Kiladis, 2008). The MJO plays a major role in the intraseasonal and interannual variability of rainfall over eastern equatorial Africa (Pohl & Camberlin, 2006).
The ITCZ Paradigm over Africa
The prevailing paradigm for the march of the seasons over Africa is the north-south migration of the Intertropical Convergence Zone (ITCZ). The original definition of this zone (Figure 7) was the near-equatorial convergence of the trade winds of the two hemispheres (Miller, 1996; Nicholson, 2013). This convergence zone is clear in global averages and over the oceans, but much less so over Africa.
Nonetheless, a paradigm for Africa ensued (Figure 8) in which the ITCZ reached the southern fringe of the Sahara in the late boreal summer then commenced a southward migration, reaching its extreme position over Southern Africa in the austral summer. Maximum rainfall was assumed to be associated with the prevailing presence of the ITCZ. Accordingly, at the northern and southern extremes of its migration, a single rainy season occurred during the course of the year. In the equatorial latitudes of Africa, throughout which the ITCZ passes twice during the courses of the year, two rainy seasons prevail (Nieuwolt, 1977). Over West Africa specifically the paradigm evolved to the picture shown in Figure 9, which is based only on limited observations from Nigeria.
The continental ITCZ paradigm appears to be substantiated by the prevailing patterns of rainfall seasonality over Africa, which show the anticipated north-south migration (Figure 10). However, the link between the seasonality and the ITCZ is markedly less clear. Over North Africa, the surface ITCZ lies well to the north of the rainfall maximum, or rain belt. Its latitudinal position does not change markedly over time, while that of the rain belt does. Also, the ITCZ’s latitude remains in North Africa well into the boreal autumn, when the rainy season occurs in equatorial latitudes. Adding to the confusion is that many authors in fact use the term ITCZ to describe the position of the rains and not the surface convergence zone at the heart of the ITCZ definition.
For these reasons it is becoming increasingly recognized that the simple ITCZ paradigm does not work well over the African continent, particularly when the term is applied to the latitudinal location of the rainfall maximum. Nicholson (2009) has suggested using the term “tropical rain belt” for this maximum. Similarly, Zhang, Woodworth, and Gu (2006) and Ross and Krishnamurti (2007) utilize the terms “rain band” and “equatorial rain belt,” respectively, in lieu of ITCZ in describing the rainfall maximum over West Africa. At least for North Africa the ITCZ paradigm is being replaced by a much more comprehensive paradigm of the West African monsoon, described in the following section.
The West African Monsoon
The monsoonal nature of West African climate has long been recognized. The initial paradigm was merely a seasonal shift between the dominance of the dry northeasterly Harmattan and the moist southwesterly flow. The seasonal shifts were of great consequence for the European explorers and settlers because the Harmattan, although heavily laden with dust, was generally a healthy time of year. In contrast, the discomfort and high humidity of the southwest monsoon brought general illness, fevers, and many diseases, such as malaria and yellow fever (Norrgärd, 2013).
The original paradigm (Figure 11) was based solely on surface conditions: a pronounced seasonal wind shift produced by thermodynamic contrasts between the Sahara and the equatorial Atlantic. Griffiths (1972) added a vertical dimension to the paradigm (Figure 9), producing a rough schematic that was long accepted but erroneous in detail. As the monsoon paradigm further evolved, it became apparent that upper-level circulation, particularly the mid-tropospheric African Easterly Jet, is an important component. Overall the main contrasts with the classic picture are the diminished emphasis on the ITCZ and the inclusion of several jet streams and shear zones, the African Easterly Waves (AEWS), the Saharan Heat Low, and Mesoscale Convective Systems, as opposed to local rainfall induced by thermal instability.
Revised images of the West African monsoon were published by Thorncroft, Nguyen, Zhang, and Payrille (2011), based on results of the AMMA (African monsoon multidisciplinary analysis) project, and by Nicholson and Grist (2003) and Nicholson (2009). Four phases of the monsoon have been defined, based on the location of the rain belt (Figure 12). Common to each is two cells of meridional overturning: a deep cell that includes the rain belt and a shallow cell with rising motion over the Sahara in the vicinity of the surface convergence zone and Saharan Heat Low.
Nicholson (2009) developed a monsoon model for the Sahelian phase only, which occurs in the boreal summer. The rain belt is seen to lie between the cores of the African Easterly Jet and the upper-tropospheric Tropical Easterly Jet (Figure 13). Also the southwesterly monsoon flow is overridden in many wet years by a westerly jet stream arising from inertial instability (Nicholson & Webster, 2007). The model (Figure 14) further shows the two meridional cells as separate entities, separated by a region of subsidence. In wet years the cells do merge, similar to what is shown in the model of Thorncroft et al. (2011). Note that in both models for the Sahelian phase, the surface convergence zone lies 10 degrees north of the rainfall maximum. The location of the convergence zone does not vary greatly from year to year, while that of the rain belt does (Nicholson & Grist, 2001).
African Easterly Waves
The easterly wave is a disturbance in the pressure field that produces north-south perturbations in the easterly winds that prevail over much of the tropics. The easterly waves over Africa are the primary trigger for hurricane development in the Atlantic. Easterly waves were first recognized in the 1940s (Riehl, 1945, 1954) but did not receive much attention prior to a series of meteorological field experiments in the 1960s and 1970s. In the case of Africa the relevant experiment was the GARP Atlantic Tropic Experiment (GATE) of the Global Atmospheric Research Program (GARP). This international experiment was conducted over West Africa and the eastern tropical Atlantic in the summer of 1974 and involved observations from ships and aircraft and ground-based instrumentation.
At the time of GATE the paradigm for easterly waves was generally that obtained from study of waves in the Caribbean (Riehl, 1954; Shapiro, Stevens, & Ciesielski, 1988). Those waves appear as triggering convection (i.e., convective precipitation) primarily behind the wave trough (Figure 15). Studies of African Easterly Waves (AEWs), which traverse the entire east-west extent of North Africa, have shown that the link between waves and convection is considerably more complex. The mesoscale systems that bring most of the rainfall in the Sahel tend to form in the northerly flow ahead of the trough (Matthews, 2004). However, the relationship depends on latitude, relationship to the African Easterly Jet, and position within the east-west extent of Africa (Gu, Adler, Huffman, & Curtis, 2004; Kiladis, Thorncroft, & Hall, 2006). Notably, the AEWs are the primary trigger for hurricane development in the Atlantic.
A major addition to the paradigm came from the work of Burpee (1972). He demonstrated that the AEWs are associated with a dynamic instability involving both baroclinic and barotropic processes. The development of this instability requires that thresholds of both horizontal and vertical wind shear be surpassed. For a long time, baroclinic-barotropic instability was assumed to be the trigger for AEWs.
During the last decade the paradigm has shifted (Nicholson, 2013), with many studies emphasizing the mutual interaction between waves and convection, the role of convection as a wave trigger, topography as a possible trigger, and the role played by additional types of waves on convection over West Africa. Many of the new results are related to the AMMA (African Monsoon Multidisciplinary Analysis) experiment that took place in 2006 (Janicot et al., 2008; Redelsperger et al., 2006). The prevailing view is that most of the waves are triggered by topography in the far eastern Sahel. Being upstream of the African Easterly Jet, baroclinic-barotropic instability is not present. As the waves move westward, into the vicinity of the AEJ, the instability facilitates the development and maintenance of the wave (Diaz & Aiyyer, 2013).
The intense precipitation associated with tropical storms and hurricanes has been known for centuries. With the exception of these systems, the traditional paradigm for tropical rainfall was that of relatively small and isolated thunderstorms being locally triggered when intense heating gives way to thermal instability. This process is termed convection, and it occurs most frequently in the afternoon, so that tropical rainfall was assumed to have an afternoon maximum. Exceptions, of course, were evident. Nieuwolt (1977), for example, shows that in Nairobi maximum rainfall occurs in the evening or at night. It is associated with convective disturbances that develop over the highlands and then descend into the lowlands when the downslope winds commence. Similarly, coastal locations tend to have a morning maximum associated with the onset of land breezes.
The extremely low spatial correlations of tropical rainfall (Jackson, 1974; Sharon, 1981) supported the view that rainfall is associated mainly with local thunderstorms. An extreme version of this concept was applied to the Sahara, with the Germans coining the term Platzregen (loosely translated as “rain in one place”) to describe the rare rain events. However, researchers soon became aware that, although tropical rainfall itself might be localized, it was nevertheless generally linked to large-scale weather patterns.
One example is the African easterly wave (AEW) described in the previous section. Some 60 waves traverse the Sahelian latitudes of West Africa each year. These produce conditions favorable for the development of precipitation. Much of the rain occurs within mesoscale features termed “cloud clusters.” These are large areas of cloud, averaging some 2 × 105 km2 over West Africa (Martin & Schreiner, 1981), that include numerous cells of intense rain.
The paradigm for tropical rainfall further evolved with the identification of large-scale systems variously termed Mesoscale Convective Complexes (MCCs) or Mesoscale Convective Systems (MCSs) (Figure 16). These might originate from the aggregation of numerous smaller systems or may be organized by wave disturbances. The average of size of an MCS is 10,000 km2 (Nesbitt, Cipelli, & Rutledge, 2006). The most intense (Figure 17) are associated with convective clouds in the afternoon, which ultimately develop into cumulonimbus clouds with the characteristic anvil (Nesbitt, Zipser, & Cecil, 2000).
The availability of satellite photos was responsible for much of the evolution of the paradigm for tropical rainfall. The importance of several types of large-scale systems, such as diagonal troughs (also termed cloud bands) and cut-off lows, was recognized. These systems are extremely important over Africa (Knippertz & Martin, 2005, 2007; Taljaard, 1985; van Heerden & Taljaard, 1998), where the former often produce unseasonal rainfall and the latter often produce flash floods.
The Tropical Rainfall Measuring Mission (TRMM) satellite produced even more dramatic changes. These underscored the importance of the MCS. While these systems comprise only about 2% of all tropical rain-bearing features, they produce about 50% of the total rainfall tropics-wide and up to 90% of the rainfall in parts of West Africa (Nesbitt et al., 2006) (Figure 18). TRMM also demonstrated that much of the rainfall in the tropics occurs at night and is of stratiform rather than convective origin. It occurs when the cumulonimbus anvil intensifies and spreads at night. This stratiform rain accounts for some 73% of the rain area and contributes roughly 40% of the total rainfall for the tropics as a whole (Schumacher & Houze, 2003).
Relationships to the Land Surface
As is the case with most continents, Africa’s climatic regime is forced by global-scale processes, including the influence of the global oceans. However, Africa is unique among the continents in that its climate, particularly climatic effects linked the land surface, can affect global climate. Examples include the triggering of hurricanes by waves the arose over West Africa and the impact of African dust on these waves. For that reason, since the 1980s considerable attention has been devoted to land surface processes over Africa.
The term “desertification” suggests the conversion of productive land into barren desert. The first application of this term to Africa is probably that of Aubreville (1949). He, as well as Stebbing (1935), suggested that the Sahara was overtaking forested areas further south, mainly as a result of human activities. This paradigm of the advancing desert at the hands of humankind (Stafford Smith & Reynolds, 2002) received much support when drought enveloped West Africa in the late 1960s and early 1970s and the United Nations (UN) in 1977 convened its first conference on the subject (UNEP, 1977). At the same time satellite photos (Figure 19) demonstrated that international borders could be delineated from space because differences in grazing practices on either side of the border created contrasts in surface albedo (i.e., the reflection of solar radiation by the surface) (Nicholson, 2011; Nicholson, Tucker, & Ba, 1998). This was interpreted as solid evidence that the desertification was anthropogenic. The UN published reports stating that 25% of the earth’s surface had been affected by the process of desertification (Figure 20) and that over West Africa the Sahara was advancing southward by some 5 to 6 km per year. The process was considered to be irreversible, and grazing practices were assumed to be a major cause.
Numerous subsequent studies (e.g., Helldén, 1991; Mainguet, 1991; Nicholson, 1990; Thomas & Middleton, 1994) pointed out the unscientific nature of these claims, and the paradigm was modified. Changes included the incorporation of climate, notably drought, as a major contributing factor (Nicholson et al., 1998; Tucker & Nicholson, 1999; Tucker, Dregne, & Newcomb, 1991; Wessels et al., 2007); the realization that the process was not necessarily irreversible (e.g., Prince et al., 1998); and revised assessments as to the extent of desertification (e.g., Prince, 2002; Prince, Brown de Colstoun, & Kravitz, 1998) (Figure 21). Notably, early estimates of Sahara “advancement” were based on temporary changes in vegetation cover induced by drought (Nicholson, 2011). The UN in 1994 revised its definition to read “land degradation in arid, semi-arid and dry subhumid areas resulting from various factors, including climatic variations and human activities” (Warren, 1996). This might include soil loss, deterioration of soil properties (e.g, loss of organic matter or nutrients, changes in soil structure), change in vegetation structure and species, or even long-term loss of natural vegetation (Walker, Abel, Stafford Smith, & Langridge, 2002). It has also been recognized that grazing practices have been greatly overestimated as a cause of land degradation (e.g., Hiernaux & Turner, 2002; Mortimore & Turner, 2005; Tiffen, Mortimore, & Gichuki, 1994).
Within the last one to two decades the paradigm has been further modified to demonstrate the complexity of the interactions and the feedbacks that take place within the ecohydrological system (e.g., D’Odorico, Bhattachan, Davis, Ravi, & Runyan, 2013; Okin, 2002; Stafford-Smith & Reynolds, 2002). At the heart of the process is disturbance of the landscape, which can result from natural phenomena such as drought or anthropogenic factors such as intentional removal of vegetation. The result is a reduction of the productivity or complexity of the ecosystem, changes in the feedback processes that produce a stable ecosystem, and, in drylands ecosystems, generally an increase in patchiness.
Several related paradigms have also emerged, such as “patch dynamics” (Meyer, Wiegand, Ward, & Moustakas, 2007), “islands of fertility” (Ridolfi, Laio, & D’Odorico, 2008), facilitation vs. competition among the different components of a dryland ecosystem (Gilad, Shachak, & Meron, 2007; Meron, Yizhaq, & Gilad, 2007), models of resilience and resistance (Carpenter, Walker, Anderies, & Abel, 2001; Walker et al., 2002), the “self-organization” of ecosystems, and “catastrophic shifts” in vegetation (Scheffer & Carpenter, 2003). “Patch dynamics” relates to natural successional changes in the heterogeneous character of semi-arid lands, with local shifts between woody and grassy vegetation over time (Figure 22). The “islands of fertility” refer to the concentration of vegetation and moisture in the “canopy” patches, where positive feedbacks among elements “facilitate” the local productivity. In contrast, negative feedbacks arise from the competition among elements, resulting in the more barren intercanopy patches. Resilience and resistance relate to the ability of an ecosystem to absorb disturbance. The concept of “self-organization” suggests that the spatial patterns of vegetation result from processes within an ecosystem rather than being externally imposed (Rietkerk, Dekker, De Ruiter, & van de Koppel, 2004). The relative magnitude of the feedback processes changes as the patchiness of a system increases. Hypothetically a state of disturbance can be reached from which recovery is not possible, i.e., an irreversible process of desertification (Rietkerk et al., 2011).
Impact of Land Surface on African Weather and Climate
The most broadly publicized paradigm for the impact of the land surface on African climate was that proposed by Charney (1975). He suggested that the exposure of high reflective soil by grazing changed surface albedo (reflectivity) (see Figure 19) in such a way as to create the drought that commenced in 1968. The paradigm of albedo changes impacting climate, especially that of Africa, became a common one (e.g., Claussen, 1997; Dickinson, 1983; Henderson-Sellers & Hughes, 1982).
The paradigm was soon modified in two ways. Charney’s hypothesis that changes in the surface could actually trigger a meteorological drought did not receive substantial support. However, it became increasingly accepted that the feedback from the land surface could act to modify natural meteorological phenomena such as drought (Entekhabi, 1995; Entekhabi, Rodriquez-Iturbe, & Bras, 1992), modulating their magnitude and/or persistence. Nicholson and Palao (1993), for example, argued that the interannual persistence of drought in the core of the Sahel was largely a result of land–atmosphere interaction. A second change to the paradigm was the inclusion of soil moisture as a major contributor to the land’s impact (e.g., Charney, Quirk, Chow, & Kornfield, 1977). The role of anthropogenic albedo changes was de-emphasized as the greater importance of feedbacks related to soil moisture became apparent (Nicholson, 2000, 2015).
The paradigm further evolved with the development of the concept of “hot spots,” geographic regions where the coupling between the land surface and the atmosphere is particularly important for meteorological processes. Initial studies attempted to identify such sites by assessing the degree of recycling of surface moisture (i.e., the contribution of local evaporation to local precipitation processes) (e.g., Brubaker, Entekhabi & Eagleson, 1993). More recently, broader characteristics have been utilized and “hot spots” have been identified (Figure 23) via climate and meteorological models with interactive land surface (Koster et al., 2004).
Currently intraseasonal and interannual variability is viewed as being influenced by both land-surface effects and large-scale meteorological and oceanic changes. The large-scale changes are accepted as the principal drivers. The land-surface effects, which are mainly local, are considered to modulate the large-scale impact. This effect is generally greatest in arid and semi-arid lands, where the temporal and spatial variability of surface characteristics is great. The impact is also generally greater in low-latitude regions, where large-scale meteorological fronts and cyclonic weather systems seldom play a role.
The image of people struggling through blowing sand has long been associated with the African deserts. Sand and dust storms are a major feature of the drier African environments. These were initially viewed as isolated and local events, but the paradigm of African dust has evolved to include large-scale dimensions and associations.
The contrast between a sand storm and a dust storm is related to the size of the materials. The larger sand particles can only be mobilized for brief periods of time and within a relatively small distance from the surface (Bagnold, 1941). The smaller silt and clay particles that comprise dust can reside in the atmosphere for long periods and can rise to extreme heights above the surface (Gillette, 1981).
Dust storms have been mentioned over many centuries in the literature concerning Africa. The “haboob,” with intense blowing dust and visibility approaching zero, has long been associated with Sudan climate, for example. This term has been adapted in the southwestern United States as well to describe major dust fronts associated with intense downdrafts from a single storm. Once regular meteorological observations commenced, quantitative estimates of the frequency of dust storms could be derived.
Around the 1950s it became recognized that dust generated by such storms could travel very long distances. Meigs (1953) published a map showing the source regions of dust and their global trajectories, identifying the semi-arid regions of Africa (principally North Africa) and elsewhere as major regions. Considerable attention was paid to what was then termed “Saharan dust.”
For North Africa the paradigm for dust evolved dramatically from the concept of localized dust storms and dust outbreaks occurring in conjunction with individual storms to major outbreaks associated with easterly waves and to the almost continual presence of a dust-infused section of atmosphere called the Saharan Air Layer (SAL; Carlson & Prospero, 1972) (Figure 24). It was further realized that the Sahara was not the major source of this dust and that the dust emanated instead primarily from Sahelian regions where fine materials lay in desiccated Holocene lake beds (Prospero, Ginoux, Torres, Nicholson, & Gill, 2002). Satellite images show the large-scale nature of the dust outbreaks (Figure 25), and current research focuses on the potential effects of the dust outbreaks and SAL on climate and storms.
Surface water balance is a global concept with basic tenets of the balance being applicable to any place on earth. In semi-arid regions, such as those that prevail over Africa, it is a particularly important feature of the environment. The paradigm used for years was simple: precipitation is balanced by runoff, evaporation, and changes in moisture storage within the soil. This same framework prevailed in most meteorological models and in this context is termed the “bucket model” (Figure 26). If the surface is adequately moist, evaporation depends on atmospheric factors, namely energy input, wind, and saturation vapor deficit (roughly speaking, relative humidity). In this case, the process is “atmosphere controlled.” Calculation of the balance requires assessment of a parameter termed potential evapotranspiration, Ep, which Thornthwaite (1948) defined as the maximum amount of evaporation that could be sustained by the given atmospheric conditions. Theoretically this represents a combination of evaporation from surfaces plus transpiration by plants. Penman (1956) recognized that Ep depends on more than atmospheric conditions and that factors such as vegetation type play a role. He added the use of a reference crop in the assessment of potential evapotranspiration.
The impact of vegetation was further developed in the paradigm, with Monteith (1965) modifying Penman’s classical Ep equation to include various vegetation terms. His paradigm, which relies on a series of “resistances” to the passage of moisture through the ecosystem (Figure 27), has largely been followed since that time. At the same time, models were developed to prescribe the evaporative process under what is termed “soil-controlled” or “water-limited” conditions, i.e., when the surface is dry enough that soil moisture levels determine evapotranspiration. The paradigm evolved to include the impact of soil texture on evapotranspiration (Dunne & Leopold, 1978).
Further evolution of the paradigm involved consideration of four reservoirs of moisture for the evaporative process (leaf surfaces, soil surface, soil moisture, and transpiration) (Figure 28), separate consideration of surface and subsurface runoff (e.g., Deardorff, 1978), and the incorporation of different timescales for the separate processes (e.g., Lettau, 1969) (see also Nicholson, Lare, Marengo, & Santos, 1996). This version of the paradigm became the basis for land–atmosphere coupling schemes in a new generation of climate model. Early schemes were the Simple-Biosphere (SiB) model (Sellers, Mintz, Sud, & Dalcher, 1986) and the Biosphere-Atmosphere Transfer Scheme (BATS) (Dickinson, Kennedy, Henderson-Sellers, & Wilson, 1986).
Increasing complexity was incorporated in the context of the interdisciplinary focus termed ecohydrology (Porporato & Rodriguez-Iturbe, 2013; Rodriguez-Iturbe, 2000). This considers feedback between the vegetation and water balance and among vegetation components of the ecosystem in much greater detail. A specific paradigm for plant–water relationships that has developed for arid lands is the “pulse-reserve” model (Ludwig, Wilcox, Breshears, Tongway, & Imeson, 2005; Noy-Meir, 1973; Reynolds, Kemp, Ogle, & Fernández, 2004). It distinguishes processes during a pulse of moisture (a precipitation event) and during the intervening drier period. This paradigm is being increasingly utilized to examine the water balance in drylands regions, such as those that prevail throughout most of the African continent.
Climate Variability and Change in Africa
The large changes in African climate over time have long been recognized. Vestiges of long-dry lakes abound through the continent. Over historical times major changes in lakes have also been described by inhabitants and European explorers. Less than a century ago it was believed that the “pluvials” (very wet periods marked by high lake levels) over Africa coincided with glacial epochs in the higher latitudes. For shorter-term changes of rainfall conditions and lake levels, sunspots were a popular explanation. This idea has lost favor, but even recently one study reported a correlation between sunspots and the multi-century record of the Nile floods (Ruzmaikin, Feynman, & Yung, 2006).
The paradigms used to explain variability over Africa on both short and long timescales have evolved markedly over time. This is due in part to the more sophisticated proxy evidence now available and to the recent changes in our understanding of the complex factors influencing rainfall over the continent. In this section, four brief examples of changing paradigms for climate change are presented. This first relates to paleoclimate. The others relate to recent climatic fluctuations over the Sahel, East Africa, and Southern Africa.
Holocene Climate in Africa
The paradigm for the pluvial/glacial correlation changed abruptly with the publication by Butzer, Isaac, Richardson, and Washbourn-Kamau (1972) of well-dated lakes throughout tropical Africa. These showed that the lakes were at very low stands or even completely desiccated during the last glacial maximum of the Pleistocene. The “pluvial” commenced as the glacial ended sometime between 15,000 and 12,000 years ago (Street & Grove, 1979). Most of the lakes studied at the time remained very deep throughout most of the Holocene (Figure 29). It further appeared that the lake-level fluctuations were more or less in phase throughout tropical Africa. This concept is now being challenged by studies showing an out-of-phase relationship between lakes in eastern and western portions of East Africa (e.g., Tierney & DeMenocal, 2013).
About the same time that Butzer et al. (1972) overturned the pluvial/glacial concept, studies from the high latitudes provided a mechanism for explaining the major glacial and interglacial cycles. Termed the Milankovitch mechanism, this involved changes in the earth–sun relationship, such as the tilt of the earth’s axis with respect to the plane of its orbit around the sun. This was also assumed to govern the changes seen over Africa as well (Kutzbach & Street-Perrott, 1985). Recently, however, Otto-Bliesner et al. (2014) showed that many of the documented changes over Africa during the late glacial and Holocene could only be explained if both the AMO and greenhouse gases were added to the paradigm.
The pluvial conditions of the Holocene were clearly evidenced not only in tropical Africa but also in the central Sahara (Kroepelin et al., 2008). Most of the now-hyperarid region was a semi-arid savanna some 5,000 years ago. This wet phase occurred synchronously with the high lake stands elsewhere over the continent. Most studies suggested that the wet phase ended gradually, with the current Sahara coming into existence roughly 3,000 years ago. An alternative hypothesis has, however, been proposed. Claussen et al. (1999), based on results of model simulations, and deMenocal et al. (2000), based on dust in sediment cores, suggested that instead the change was much more rapid, perhaps occurring within a few hundred years. Ecological theory supports this possibility (e.g., Scheffer & Carpenter, 2003). However, the verdict is still out on this paradigm for change in the Sahara. The issue is further discussed in the contribution to this encyclopedia by Claussen on abrupt change.
Attention was first focused on climate variability in the Sahel when a severe drought occurred in the late 1960s and early 1970s. The first meteorological paradigm for explaining the drought was an anomalous southward displacement of the ITCZ (e.g., Kraus, 1977; Winstanley, 1973). Despite scanty evidence for this paradigm, this explanation persisted for quite some time. At the same time, Charney (1975) suggested that the origin was anthropogenic.
Both hypotheses were discounted in papers by Nicholson (1981, 1986) showing that the drought was not localized (as required by the Charney hypothesis) but instead continental in scale and was associated with a weakening of the ITCZ/rain belt rather than a southward displacement. The paradigm was further modified to include the influence of sea-surface temperatures (SSTs). Lamb (1978a, 1978b) showed that the Sahel drought was associated with an SST dipole (Figure 30), with abnormally warm temperatures in the Gulf of Guinea, and abnormally low SSTs over the eastern Atlantic in latitudes near the Sahel. Consistent with this, Folland, Palmer, and Parker (1986) showed a link to interhemispheric SST contrasts. El Niño was also suggested as a factor in drought (e.g., Janicot, Trzaska, & Poccard, 2001). The dipole and related interhemispheric contrast paradigm still hold, but links that have been shown to ENSO are more controversial. Overall, there is clear evidence of an influence of all three major oceans and of the Mediterranean (Nicholson, 2013). However, the importance of each depends on the timescale considered (e.g., interannual vs. interdecadal) and also changes over time. On the latter timescale, the AMO (Fig 31) appears to be the most important factor in variability (Zhang & Delworth, 2006).
Nicholson and Grist (2001) and Nicholson (2008) added direct regional factors to the paradigm. They interpreted the interannual variability of rainfall in terms of changes in intensity and/or latitudinal displace of the tropical rain belt (i.e., the zone referred to by many as the ITCZ). It was shown to lie between the cores of the African Easterly Jet (AEJ) and Tropical Easterly Jet (TEJ). The intensity of the latter is a crucial factor determining the intensity of the rain belt while the location of the AEJ is critical in determining its latitude. Nicholson and Webster (2007) added to this paradigm by demonstrating that inertial instability related to cross-equatorial pressure gradients served to displace the AEJ and rain belt northward into the Sahel.
Notably, the ideas of Charney have not been totally discounted. Rainfall in the Sahel exhibited an extreme multi-decadal persistence of anomalous conditions. This was cited by some as evidence of the impact of the land surface, particularly soil moisture (e.g., Nicholson & Palao, 1993; Shukla, 1995). The current paradigm is that West Africa’s summer monsoon responds to ocean forcing that is amplified by land–atmosphere interaction (Giannini, Biasutti, & Verstraete, 2008).
The East African “Short Rains” of the Boreal Autumn
Most of East Africa experiences two rainy seasons during the course of the year. The so-called “long rains” of March to April produce most of the rainfall, but numerous studies clearly documented that the “short rains” of October–November are much more variable and produce most of the interannual variability. It was also more or less universally accepted that the variability of the short rains is very closely tied to ENSO (Figure 32), with el Niño producing wet conditions and la Niña producing drought.
Several studies helped to change this picture. Goddard and Graham (1999) and Nicholson et al. (2001) showed that the ENSO signal in eastern and southern Africa is manifested as changes in SSTs in the Atlantic and Indian Oceans. In the absence of that response, ENSO events did not modulate rainfall. Hastenrath (2000) further contributed to this understanding by showing that for the “short rains” the relevant factor is the intensity of low-level equatorial westerlies. A deceleration of the westerlies would weaken the descending branch of the Walker cell, allowing for increased rainfall over East Africa. While that factor is generally associated with ENSO, these westerlies are governed by other factors as well, including the Indian Ocean Dipole (also called the Indian Ocean Zonal Mode, or IOZM).
Saji et al. (1999) and Webster, Moore, Loschnigg, and Leben (1999) provided evidence that, in fact, the IOZM is the major driver in interannual variability of the “short rains” (see also Behera et al., 2005; Clark, Webster, & Cole, 2003). In concurrence with Hastenrath (2000), both papers underscored the importance of zonal circulations in the equatorial Indian Ocean in governing interannual variability in this season. Liebmann et al. (2014) and Nicholson (2015) likewise found the correlation to be markedly higher with the IOZM than with ENSO. Nicholson (2015) further found, in agreement with Hastenrath (2000, 2007b), that the most important factor is the low-level equatorial westerlies. The correlation with October–November East African rainfall over the 139 years from 1874 to 2012 is—.74. More importantly, it is clear that the importance of each of the three factors—the IOZM, the westerlies, and ENSO—changes with time and in an independent fashion (Figure 33). Hence a simple paradigm with any single dominant factor is not realistic.
It was likewise assumed that ENSO was the major driver of variability in Southern Africa (Figure 32). As with ENSO and East Africa, it became clear that an expanded paradigm was necessary. Nicholson and Entekhabi (1987), Reason (1998), and Williams, Kniveton, and Layberry (2008) showed that SSTs in the southeastern Atlantic also play a major role. Washington and Preston (2006) demonstrated the importance of an SST dipole in western tropical Indian Ocean. This dipole explains two of the wettest years on record, 1974 and 1976. Hence the paradigm for rainfall variability in Southern Africa must include all three ocean basins. However, the Atlantic and Indian Oceans probably play the greatest role. Drought, which was traditionally associated with El Niño events, generally occurs only in conjunction with events that produce a warming in the Atlantic and Indian Oceans (Nicholson et al., 2001).
Arguez, A., & Vose, R. S. (2011). The definition of the standard WMO climatic normal. Bulletin of the American Meteorological Society, 92, 699–704.Find this resource:
Aubreville, A. (1949). Climats, forêts et désertification de l’Afrique tropicale. Paris: Soc. Ed. Géogr. Marit. et Colon.Find this resource:
Bagnold, R. A. (1941). The physics of blown sand and desert dunes. London: Methuen.Find this resource:
Behera, S. K., Luo, J. J., Masson, S., Rao, S. A., Sakum, H., Yamagata, T., et al. (2005). Paramount impact of the Indian Ocean dipole on the East African short rains: A CGCM study. Journal of Climate, 18, 4514–4530.Find this resource:
Bjerknes, J. (1969). Atmospheric teleconnections from the equatorial Pacific. Monthly Weather Review, 97, 163–172.Find this resource:
Borius, A. (1875). Recherches sur le climat du Sénégal. Paris: Gauthier Villars.Find this resource:
Borius, A (1880). Nouvelles recherches sur le climat du Sénégal. Paris: Gauthier Villars.Find this resource:
Brubaker, K. L., Entekhabi, D., & Eagleson, P.S. (1993). Estimation of continental precipitation recycling. Journal of Climate, 6, 1077–1089.Find this resource:
Budyko, M. I. (1986). The evolution of the biosphere. Dordrecht: Reidel.Find this resource:
Burpee, R. W. (1972). Origin and structure of easterly waves in lower troposphere of North Africa. Journal of the Atmospheric Sciences, 29, 77–90.Find this resource:
Butzer, K. W., Isaac, G. L., Richardson, J. L., & Washbourn-Kamau, C. (1972). Radiocarbon dating of East African lake levels. Science, 175, 1069–1076.Find this resource:
Carlson, T. N., & Prospero, J. M. (1972). The large-scale movement of Saharan air outbreaks over the equatorial North Atlantic. Journal of Applied Meteorology, 11, 283–297.Find this resource:
Carpenter, S., Walker, B., Anderies, J. M., & Abel, N. (2001). From metaphor to measurement). resilience of what to what? Ecosystems, 4, 765–781.Find this resource:
Charney, J., Quirk, W. J., Chow, S. H., & Kornfield, J. (1977). A comparative study of effects of albedo change on drought in semi-arid regions. Journal of the Atmospheric Sciences, 34, 1366–1385.Find this resource:
Charney, J. G. (1975). The dynamics of deserts and droughts. Quarterly Journal of the Royal Meteorological Society, 101, 193–202.Find this resource:
Clark, C. O., Webster, P. J., & Cole, J. E. (2003). Interdecadal variability of the relationship between the Indian Ocean zonal mode and East African coastal rainfall anomalies. Journal of Climate, 16, 548–554.Find this resource:
Claussen, M. (1997). Modeling the bio-geophysical feedback in the African and Indian monsoon region. Climate Dynamics, 13, 247–257.Find this resource:
Claussen, M., Kubatzki, C., Brovkin, V., Ganopolski, A., Hoelzmann, P., & Pachur, H. J. (1999). Simulation of an abrupt change in Saharan vegetation in the mid-Holocene. Geophysical Research Letters, 26, 2037–2040.Find this resource:
Deardorff, J. W. (1978). Efficient prediction of ground surface-temperature and moisture, with inclusion of a layer of vegetation. Journal of Geophysical Research—Atmospheres, 83, 1889–1903.Find this resource:
Diaz, M., & Aiyyer, A. (2013). The genesis of African easterly waves by upstream development. Journal of the Atmospheric Sciences, 70, 3492–3512.Find this resource:
Dickinson, R. E. (1983). Land surface processes and climate—surface albedos and energy balance. Advances in Geophysics, 25, 305–353.Find this resource:
Dickinson, R. E., Kennedy, P. J., Henderson-Sellers, A., & Wilson, M. (1986). Biosphere-atmosphere transfers scheme (BATS) for the NCAR Community Climate Model. Technical Report NCARE/TN-275+STR. Boulder, CO: National Center for Atmospheric Research.Find this resource:
D’Odorico, P., Bhattachan, A., Davis, K. F., Ravi, S., & Runyan, C. W. (2013). Global desertification: Drivers and feedbacks. Advances in Water Resources, 51, 326–344.Find this resource:
Dunne, T., & Leopold, L. B. (1978). Water in environmental planning. New York: W. H. Freeman.Find this resource:
Entekhabi, D. (1995). Recent advances in land-atmosphere interaction research. U. S. Natl. Rep. Int. Union Geod. Geophys. 1991–1994. Reviews of Geophyics, 995–1003.Find this resource:
Entekhabi, D., Rodriguez-Iturbe, I., & Bras, R.L. (1992). Variability in large-scale water balance with land surface–atmosphere interaction. Journal of Climate, 5, 798–813.Find this resource:
Fitzner, R. (1907). Regenverteilung in den deutschen Kolonien. Berlin: Herman Paetel.Find this resource:
Flohn, H. (1959). Equatorial westerlies over Africa, their extension and significance. In D. J. Bargman (Ed.), Tropical meteorology in Africa (pp. 253–264). Nairobi: Munitalp Foundation.Find this resource:
Flohn, H. (1971). Tropical circulation patterns. Bonner Meteorologische Abhandlungen, 15, 1–55.Find this resource:
Folland, C. K., Palmer, T. N., & Parker, D. E. (1986). Sahel rainfall and worldwide sea temperatures, 1901–1985. Journal of Forecasting, 1, 21–56.Find this resource:
Giannini, A., Biasutti, M., & Verstraete, M. (2008). A climate model-based review of drought in the Sahel. Desertification, the re-greening and climate change. Global and Planetary Change, 64, 119–128.Find this resource:
Giannini, A., Saravanan, R., & Chang, P. (2003). Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science, 302, 1027–1030.Find this resource:
Gilad, E., Shachak, M., & Meron, E. (2007). Dynamics and spatial organization of plant communities in water-limited systems. Theoretical and Population Biology, 72, 214–230.Find this resource:
Gillette, D. A. (1981). Production of dust that may be carried great distances. Geological Society of America, 186, 11–26.Find this resource:
Ginestous, G. (1903). Etudes sur le climat de la Tunisie. Tunis: G. Guinle.Find this resource:
Goddard, L., & Graham, N. E. (1999). Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. Journal of Geophysical Research, 104, 19099–19116.Find this resource:
Griffiths, J. F. (1972). World Survey of Climatology (vol. 10). Elsevier.Find this resource:
Gu, G., Adler, R. F., Huffman, G. J., & Curtis, S. (2004). African easterly waves and their association with precipitation. Journal of Geophysical Research D, 109, Article ID D04101.Find this resource:
Hastenrath, S. (2000). Zonal circulations over the equatorial Indian Ocean. Journal of Climate, 13, 2746–2756.Find this resource:
Hastenrath, S. (2007a). Equatorial zonal circulations: Historical perspectives. Dynamics of Atmospheres and Oceans, 43, 16–24.Find this resource:
Hastenrath, S. (2007b). Circulation mechanisms of climate anomalies in East Africa and the equatorial Indian Ocean. Dynamics of Atmospheres and Oceans, 43, 25–35.Find this resource:
van Heerden, J., & Taljaard, J. J. (1998). Africa and the surrounding waters. In Meteorology of the Southern Hemisphere (pp. 141–174). Boston: American Meteorological Society.Find this resource:
Helldén, U. (1991). Desertification—time for an assessment? Ambio, 20, 372–383.Find this resource:
Henderson-Sellers, A., & Hughes, N. A. (1982). Albedo and its importance in climate theory. Progress in Physical Geography, 6, 1–44.Find this resource:
Hiernaux, P., & Turner, M. D. (2002). The influence of farmer and pastoralist manage practices on desertification processes in the Sahel. In J. F. Reynolds & D. M. Stafford Smith (Eds.), Global desertification. Do humans cause deserts? (pp. 135–148). Berlin: Dahlem University Press.Find this resource:
Holton, J. R. (2004). Introduction to dynamic meteorology. Amsterdam: Elsevier Academic Press.Find this resource:
Houghton, D. D. (2002). Introduction to climate change. Lecture notes for meteorologists. WMO, No. 926, Geneva.Find this resource:
Jackson, I. J. (1974). Inter-station rainfall correlation under tropical conditions. Catena, 1, 235–236.Find this resource:
Janicot, S., Thorncroft, C. D., Ali, A., Asencio, N., Berry, G., Bock, O., et al. (2008). Large-scale overview of the summer monsoon over West Africa during the AMMA field experiment in 2006. Annales Geophysicae, 26, 2569–2595.Find this resource:
Janicot, S., Mounier, F., Gervois, S., Sultan, B., & Kiladis, G. N. (2010). The dynamics of the West African monsoon—Part V: The detection and role of the dominant modes of convectively coupled equatorial Rossby waves. Journal of Climate, 23, 4005–4024.Find this resource:
Janicot, S., Trzaska, S., & Poccard, I. (2001). Summer-Sahel-ENSO teleconnection and decadal time scale SST variations. Climate Dynamics, 18, 303–320.Find this resource:
Jewell, R. (1981). The Bergen school of meteorology—the cradle of modern weather forecasting. Bulletin of the American Meteorological Society, 62, 824–830.Find this resource:
Johnson, D. H., & Mörth, H. T. (1962). Forecasting research in East Africa. Proc. Munitalp Symp., Nairobi, Kenya, WMO and the Munitalp Foundation, pp. 56–137.Find this resource:
Kiladis, G. N., Thorncroft, C. D., & Hall, N. M. J. (2006). Three-dimensional structure and dynamics of African easterly waves. Part I: Observations. Journal of the Atmospheric Sciences, 63, 2212–2230.Find this resource:
Knippertz, P., & Martin, J. E. (2005). Tropical plumes and extreme precipitation in subtropical and tropical West Africa. Quarterly Journal of the Royal Meteorological Society, 112, 2337–2365.Find this resource:
Knippertz, P., & Martin, J. E. (2007). The role of dynamic and diabatic processes in the generation of cut-off lows over Northwest Africa. Meteorology and Atmospheric Physics, 96, 3–19.Find this resource:
Koeppen, W. (1918). Klassification der Klimate nach Temperature, Niederschlag und Jahreslauf. Petermanns Geographische Mitteilungen, 64, 193–203.Find this resource:
Koster, R. D., Dirmeyer, P. A., Guo, Z. C., Bonan, G., Chan, E., Cox, P., et al. (2004). Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140.Find this resource:
Kraus, E. B. (1977). Subtropical droughts and cross-equatorial energy transports. Monthly Weather Review, 105, 1009–1018.Find this resource:
Kröpelin, S., Verschuren, D., Lezine, A. -M., Eggermont, H., Cocquyt, C., Francus, P., et al. (2008). Climate-driven ecosystem succession in the Sahara: The past 6000 years. Science, 320, 765–768.Find this resource:
Kutzbach, J. E. (1976). The nature of climate and climatic variations. Quaternary Research, 6, 471–480.Find this resource:
Kutzbach, J. E., & Street-Perrott, F. A. (1985). Milankovitch forcing mechanisms in the level of tropical lakes from 18 to 0 kyr BP. Nature, 317, 130–134.Find this resource:
Lamb, P. J. (1978a). Case studies of tropical Atlantic surface circulation pattern s during recent sub-Saharan weather anomalies: 1967 and 1968. Monthly Weather Review, 106, 482–491.Find this resource:
Lamb, P. J., (1978b). Large-scale tropical surface circulation patterns associated with Subsaharan weather anomalies. Tellus, 30, 240–251.Find this resource:
Lettau, H. (1969). Evapotranspiration climatonomy: I. A new approach to numerical prediction of monthly evapotranspiration, runoff, and soil moisture storage. Monthly Weather Review, 97, 691–699.Find this resource:
Lettau, H. H. (1956). Theoretical notes on the dynamics of the equatorial atmosphere. Beiträge zur Physik der Atmosphäre, 29, 107.Find this resource:
Lockwood, J. G. (1974). World climatology: An environmental approach. New York: St. Martin’s Press.Find this resource:
Liebmann, B., Hoerling, M. P., Funk, C., Blade, I., Dole, R. M., Allured, D., et al. (2014). Understanding recent eastern Horn of Africa rainfall variability and change. Journal of Climate, 27, 8630–8645.Find this resource:
Ludwig, J. A., Wilcox, B. P., Breshears, D. D., Tongway, D. J., & Imeson, A. C. (2005). Vegetation patches and runoff–erosion as interacting ecohydrological processes in semiarid landscapes. Ecology, 86, 288–297.Find this resource:
Madden, R. A., & Julian, P. R. (1972). Description of global-scale circulation cells in the tropics with a 40–50-day period. Journal of the Atmospheric Sciences, 29, 1109–1123.Find this resource:
Madden, R. A., & Julian, P. R. (1994). Observations of the 40–50-day tropical oscillation—A review. Monthly Weather Review, 122, 814–837.Find this resource:
Mainguet, M. (1991). Desertification, natural background and human mismanagement. Berlin: Springer Verlag.Find this resource:
Martin, D. W., & Schreiner, A. J. (1981). Characteristics of West African and East Atlantic cloud clusters—a survey from GATE. Monthly Weather Review, 109, 1671–1688.Find this resource:
Matthews, A. J. (2004). Intraseasonal variability over tropical Africa during northern summer. Journal of Climate, 17, 2427–2440.Find this resource:
Meigs, P. (1953). The distribution of arid and semi-arid homoclimates. In UNESCO, Arid Zone Research, Series 1, Reviews of Research on Arid Zone Hydrology (pp. 203–210).Find this resource:
Mekonnen, A., Thorncroft, C. D., Aiyyer, A. R., & Kiladis, G. N. (2008). Convectively coupled Kelvin waves over tropical Africa during the boreal summer. Structure and variability. Journal of Climate, 21, 6649–6667.Find this resource:
deMenocal, P., Ortiz, J., Guilderson, T., Adkins, J., Sarnthein, M., Baker, L., et al. (2000). Abrupt onset and termination of the African humid period: Rapid climate responses to gradual insolation forcing. Quaternary Science Reviews, 19, 347–361.Find this resource:
Meron, E., Yizhaq, H., & Gilad, E. (2007). Localized structures in dryland vegetation: Forms and functions. Chaos, 17, 037109.Find this resource:
Meyer, K. M., Wiegand, K., Ward, D., & Moustakas, A. (2007). The rhythm of savanna patch dynamics. Journal of Ecology, 95, 1306–1315.Find this resource:
Miller, R. L. (1996). The intertropical convergence zone. In S. H. Schneider (Ed.), Encyclopedia of climate and weather (vol. 1, pp. 445–448).Find this resource:
Monteith, J. L. (1965). Evaporation and the environment. In G. E. Fogg (Ed.), Symposium of the Society for Experimental Biology, the state and movement of water in living organisms (vol. 19, pp. 205–254). New York: Academic Press.Find this resource:
Mortimore, M., & Turner, B. (2005). Does the Sahelian smallholders’ management of woodland, farm trees, rangeland support the hypothesis of human-induced desertification? Journal of Arid Environments, 63, 567–595.Find this resource:
Nesbitt, S. W., Cipelli, R., & Rutledge, S. A. (2006). Storm morphology and rainfall characteristics of TRMM precipitation features. Monthly Weather Review, 134, 2702–2721.Find this resource:
Nesbitt, S. W., Zipser, E. J., & Cecil, D. J. (2000). A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. Journal of Climate, 13, 4087–4106.Find this resource:
Newell, R. E. (1979). Climate and the ocean. American Scientist, 67, 405–416.Find this resource:
Nicholson, S. E. (1981). Rainfall and atmospheric circulation during drought periods and wetter years in West Africa. Monthly Weather Review, 109, 2191–2208.Find this resource:
Nicholson, S. E. (1986). The spatial coherence of African rainfall anomalies: Interhemispheric teleconnections. Journal of Climate and Applied Meteorology, 25, 1365–1381.Find this resource:
Nicholson, S. E. (1990). The need for a reappraisal of the question of large-scale desertification: Some arguments based on consideration of rainfall fluctuations. Report of the SAREC-Lund International Meeting on Desertification, December 1990, Lund, Sweden.Find this resource:
Nicholson, S. E. (2000). Land surface processes and Sahel climate. Reviews of Geophysics, 38, 117–139.Find this resource:
Nicholson, S. E. (2008). The intensity, location and structure of the tropical rainbelt over West Africa as factors in interannual variability. International Journal of Climatology, 28, 1775–1785.Find this resource:
Nicholson, S. E. (2009). A revised picture of the structure of the “monsoon” and land ITCZ over West Africa. Climate Dynamics, 32, 1155–1171.Find this resource:
Nicholson, S. E. (2011). Dryland climatology. Cambridge, U.K.: Cambridge University Press.Find this resource:
Nicholson, S. E. (2013). The West African Sahel: A review of recent studies on the rainfall regime and its interannual variability. ISRN Meteorology.Find this resource:
Nicholson, S. E. (2015). Long-term variability of the East African “short rains” and its links to large-scale factors. International Journal of Climatology, 35, 3979–3990.Find this resource:
Nicholson, S. E. (2015). Long-term variability of the “short rains” and its links to large-scale factors. International Journal of Climatology, 35, 3979–3990.Find this resource:
Nicholson, S. E., & Entekhabi, D. (1986). The quasi-periodic behavior of rainfall variability in Africa and its relationship to the Southern Oscillation. Archives for Meteorology, Geophysics, Bioclimatology, Ser. A, 34, 311–348.Find this resource:
Nicholson, S. E., & Entekhabi, D. (1987). Rainfall variability in equatorial and southern Africa: Relationships with sea surface temperatures along the southwestern coast of Africa. Journal of Climate and Applied Meteorology, 26, 561–578.Find this resource:
Nicholson, S. E., & Grist, J. P. (2001). A conceptual model for understanding rainfall variability in the West African Sahel on interannual and interdecadal timescales. International Journal of Climatology, 21, 1733–1757.Find this resource:
Nicholson, S. E., & Grist, J. P. (2003). The seasonal evolution of the atmospheric circulation over West Africa and equatorial Africa. Journal of Climate, 16, 1013–1030.Find this resource:
Nicholson, S. E., Kim, J. & Hoopingarner, J. (1988). Atlas of African rainfall and its interannual variability. Tallahassee: Florida State University.Find this resource:
Nicholson, S. E., Klotter, D., & Dezfuli, A. K. (2012). Spatial reconstruction of semi-quantitative precipitation fields over Africa during the nineteenth century from documentary evidence and gauge data. Quaternary Research, 78, 13–23.Find this resource:
Nicholson, S. E., Lare, A. R., Marengo, J. A., & Santos, P. (1996). A revised version of Lettau’s Evapoclimatonomy model. Journal of Applied Meteorology, 35, 549–561.Find this resource:
Nicholson, S. E., Leposo, D., & Grist, J. (2001). On the relationship between El Niño and drought over Botswana. Journal of Climate, 14, 323–325.Find this resource:
Nicholson, S. E., & Palao, I. M. (1993). A re-evaluation of rainfall variability in the Sahel. Part I. Characteristics of rainfall fluctuations. International Journal of Climatology, 13(4), 371–389.Find this resource:
Nicholson, S. E., Tucker, C. J., & Ba, M. B. (1998). Desertification, drought and surface vegetation: An example from the West African Sahel. Bulletin of the American Meteorological Society, 79, 815–829.Find this resource:
Nicholson, S. E., & Webster, P. J. (2007). A physical basis for the interannual variability of rainfall in the Sahel. Quarterly Journal of the Royal Meteorological Society, 133, 2065–2084.Find this resource:
Nieuwolt, S. (1977). Tropical climatology. New York: John Wiley.Find this resource:
Norrgärd, S. (2013). A new climatic periodization of the Gold and Guinea Coasts in West Africa, 1750–1798. Åbo, Finland: Åbo Akademi University Press.Find this resource:
Noy-Meir, I. (1973). Desert ecosystems environment and producers. Annual Review of Ecological Systems, 4, 25–51.Find this resource:
Okin, G. S. (2002). Toward a unified view of biophysical land degradation processes in arid and semi-arid lands. In J. F. Reynolds & D. M. Stafford Smith (Eds.), Global desertification: Do humans cause deserts? (pp. 95–110). Berlin: Dahlem University Press.Find this resource:
Otto-Bliesner, B. L., Russell, J. M., Clark, P. U., Liu, Z., Overpeck, J., Konecky, B., et al. (2014). Coherent changes of northern and eastern equatorial Africa rainfall during the last deglaciation. Science, 346, 1223–1227.Find this resource:
Penman, H. L. (1956). Evaporation: An introductory survey. Netherlands Journal of Agricultural Science, 4, 7–29.Find this resource:
Pohl, B., & Camberlin, P. (2006). Influence of the Madden-Julian Oscillation on East Africa rainfall. II: March-May season extremes and interannual variability. Quarterly Journal of the Royal Meteorological Society, 132, 25401–2559.Find this resource:
Porporato, A., & Rodriguez-Iturbe, I. (2013). Ecohydrology Bearings—invited commentary from random variability to ordered structures: a search for general synthesis in ecohydrology. Ecohydrology, 6, 333–342.Find this resource:
Prince, S. D. (2002). Spatial and temporal scales for detection of desertification. In J. F. Reynolds & D. M. Stafford Smith (Eds.), Global Desertification: Do Humans Cause Deserts? (pp. 23–40). Berlin: Dahlem University Press.Find this resource:
Prince, S. D., Brown de Colstoun, E., & Kravitz, L. (1998). Evidence from rain use efficiencies does not support extensive Sahelian desertification. Global Change Biology, 4, 359–373.Find this resource:
Prince, S. D., K. J. Wessels, C. J. Tucker, S. E. Nicholson (2007). Desertification in the Sahel: a reinterpretation of a reinterpretation. Global Change Biology, 13, 1308–1313.Find this resource:
Prospero, J. M., Ginoux, P., Torres, O., Nicholson, S. E., & Gill, T. E. (2002). Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Reviews of Geophysics, 40, 1–31.Find this resource:
Rasmussen, E. M. (1987). Global climate change and variability: Effects on drought and desertification in Africa. In M. H. Glantz (Ed.), Drought and hunger in Africa: Denying famine a future (pp. 3–22). Cambridge, U.K.: Cambridge University Press.Find this resource:
Raulin, V. (1876). Observations pluviometriques faites dans l’Algérie, 1751–1870. Paris: Savy.Find this resource:
Raulin, V. (1882). Observations pluviometriques faites dans l’Algérie, 1871–1880. Paris: Savy.Find this resource:
Reason, C. J. C. (1998). Warm and cold events in the southeast Atlantic southwest Indian Ocean region and potential impacts on circulation and rainfall over southern Africa. Meteorology and Atmospheric Physics, 69, 49–65.Find this resource:
Redelsperger, J. L., Thorncroft, C. D., Diedhiou, A., Lebel, T., Parker, D. J., & Polcher, J. (2006). African Monsoon Multidisciplinary Analysis: An international research project and field campaign. Bulletin of the American Meteorological Society, 87, 1739–1746.Find this resource:
Reynolds, J. F., Kemp, P. R., Ogle, K., & Fernández, R. J. (2004). Modifying the “pulse-reserve” paradigm for deserts of North America: Precipitation pulses, soil water, and plant responses. Oecologia, 141, 194–210.Find this resource:
Ridolfi, L., Laio, F., & D’Odorico, P. (2008). Fertility island formation and evolution in dryland ecosystems. Ecology and Society, 13, 5.Find this resource:
Riehl, H. (1945). Waves on the easterlies and the polar front in the tropics. Misc. Rep. 17, Dept. of Meteorology, University of Chicago.Find this resource:
Riehl, H. (1954). Tropical meteorology. New York: McGraw-Hill.Find this resource:
Rietkerk, M., Brovkin, V., van Bodegom, P. M., Claussen, M., Dekker, S. C., Dijkstra, H. A., et al. (2011). Local ecosystem feedbacks and critical transitions in the climate. Ecological Complexity, 8, 223–228.Find this resource:
Rietkerk, M., Dekker, S. C., De Ruiter, P. C., & van de Koppel, J. (2004). Self-organized patchiness and catastrophic shifts in ecosystems. Science, 305, 1926–1929.Find this resource:
Rodriguez-Iturbe, I. (2000). Ecohydrology: A hydrologic perspective of climate-soil-vegetation dynamics. Water Resources Research, 36, 3–9.Find this resource:
Ross, R. S., & Krishnamurti, T. N. (2007). Low-level African easterly wave activity and its relation to Atlantic tropical cyclogenesis in (2001). Monthly Weather Review, 135, 3950–3964.Find this resource:
Ruzmaikin, A., Feynman, J., & Yung, Y. L. (2006). Is solar variability reflected in the Nile River?. Journal of Geophysical Research-Atmospheres, 111.Find this resource:
Saji, N. H., Goswami, B. N. H., Vinayachandran, P. N., & Yamagata, Y. (1999). A dipole mode in the tropical Indian Ocean. Nature, 401, 360–363.Find this resource:
Scheffer, M. M., & Carpenter, S. R. (2003). Catastrophic regime shifts in ecosystems: linking theory to observation. Trends in Ecology and Evolution, 18, 648–656.Find this resource:
Schumacher, C., & Houze, R. A., Jr. (2003). Stratiform rain in the tropics as seen by the TRMM precipitation radar. Journal of Climate, 16, 1739–1756.Find this resource:
Sellers, P. J., Mintz, Y., Sud, Y. C., & Dalcher, A. (1986). A simple biosphere model (SiB) for use within general-circulation models. Journal of the Atmospheric Sciences, 43, 505–531.Find this resource:
Serra, Y. L., Jiang, X., Tian, B., Amador-Astua, J., Maloney, E. D., & Kiladis, G. N. (2014). Tropical intraseasonal modes of the atmosphere. Annual Review of Enviromental Resources, 39, 189–215.Find this resource:
Serra, Y. L., Kiladis, G. N., & Cronin, M. G. (2008). Horizontal and vertical structure of easterly waves in the Pacific ITCZ. Journal of the Atmospheric Sciences, 65, 1266–1284.Find this resource:
Shapiro, L. J., Stevens, D. E., & Ciesielski, P. E. (1988). A comparison of observed and model-derived structures of Caribbean easterly waves. Monthly Weather Review, 116, 921–938.Find this resource:
Sharon, D. (1981). The distribution in space of local rainfall in the Namib desert. Journal of Climatology, 1, 69–75.Find this resource:
Shukla, J. (1981). Dynamical predictability of monthly means. Journal of the Atmospheric Sciences, 38, 2547–2572.Find this resource:
Shukla, J. (1995). On the initiation and persistence of the Sahel drought. In Natural climate variability on decade-to-century time scales (pp. 44–48). Washington, DC: National Academy Press.Find this resource:
Stafford Smith, D. M., & Reynolds, J. F. (2002). The Dahlem Desertification Paradigm: A new approach to an old problem. In J. F. Reynolds & D. M. Stafford Smith (Eds.), Global desertification: Do humans cause deserts? (pp. 403–424). Berlin: Dahlem University Press.Find this resource:
Stebbing, E. P. (1935). The encroaching Sahara. Geographical Journal, 86, 509–510.Find this resource:
Strahler, A. N. (1978). Physical geography (3d ed.). New York: John Wiley.Find this resource:
Street, F. A., & Grove, A. T. (1979). Environmental and climatic implications of late Quaternary lake-level fluctuations in Africa. Nature, 261, 385–390.Find this resource:
Supan, A. (1898). Die Verteiliung des Niederschlags auf der festen Erdoberfläche. Petermanns Mitt., Ergänzungsheft 124.Find this resource:
Taljaard, J. J. (1985). Cut-off lows in the South African region. Technical Paper No. 14, South African Weather Bureau, Pretoria.Find this resource:
Thomas, D. S. G., & Middleton, N. J. (1994). Desertification: Exploding the myth. Chichester, U.K.: John Wiley.Find this resource:
Thorncroft, C. D., Nguyen, H., Zhang, C., & Peyrille, P. (2011). Annual cycle of the West African monsoon: Regional circulations and associated water vapour transport. Quarterly Journal of the Royal Meteorological Society, 137, 129–147.Find this resource:
Thornthwaite, C. W. (1933). The climates of the earth: A new and original method of climate classification. Geographical Review, 23, 433–440.Find this resource:
Thornthwaite, C. W. (1948). An approach towards a rational classification of climate. Geographical Review, 38, 55–94.Find this resource:
Tierney, J. E., & deMenocal, P. B. (2013). Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science, 342, 843–846.Find this resource:
Tiffen, M., Mortimore, M., & Gichuki, F. (1994). More people, less erosion, environmental recovery in Kenya. Chichester, U.K.: Wiley.Find this resource:
Trenberth, K. E. (1976). Spatial and temporal variations of Southern Oscillation. Quarterly Journal of the Royal Meteorological Society, 102, 639–653.Find this resource:
Trewartha, G. T. (1961). The earth’s problem climates. Madison: University of Wisconsin Press.Find this resource:
Trewartha, G. T., & Horn, L. H. (1980). An introduction to climate. New York: McGraw-Hill.Find this resource:
Tucker, C. J., Dregne, H. E., & Newcomb, W. W. (1991). Expansion and contraction of the Sahara Desert from 1980 to 1990. Science, 253, 299–301.Find this resource:
Tucker, C. J., & Nicholson, S. E. (1999). Variations in the size of the Sahara desert from 1980 to 1997. Ambio, 28, 587–591.Find this resource:
UNEP (United Nations Environmental Program). (1977). Draft plan of action to combat desertification. UN Conf. on Desertification, Background Document, Nairobi.Find this resource:
Von Humboldt, A. (1845). KOSMOS: A general survey of physical phenomena of the universe. London: H. Bailliere.Find this resource:
Walker, B. H., Abel, N., Stafford Smith, D. M., & Langridge, J. (2002). A framework for the determinants of degradation in arid ecosystems. In J. F. Reynolds & D. M. Stafford Smith (Eds.), Global desertification: Do humans cause deserts? (pp. 75–94). Berlin: Dahlem University Press.Find this resource:
Walker, G. T. (1924). Correlation in seasonal variations of weather IX. Mem. India Meteor. Dept., 24, 275–332.Find this resource:
Warren, A. (1996). Desertification. In W. M. Adams, A. S. Goudie, & A. R. Orme (Eds.), The Physical Geography of Africa (pp. 342–355). Oxford: Oxford University Press.Find this resource:
Washington, R., & Preston, A. (2006). Extreme wet years over southern Africa: Role of Indian Ocean sea surface temperatures Journal of Geophysical Research-Atmospheres, 111, D15104.Find this resource:
Webster, P. J., Moore, A. M., Loschnigg, J. P., & Leben, R. R. (1999). Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401, 356–360.Find this resource:
Wessels K. J., Prince, S. D., Malherbe, J., Small, J., Frost, P. E., & VanZyl, D. (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68, 271–297.Find this resource:
Wheeler, M., & Kiladis, G. N. (1999). Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain. Journal of the Atmospheric Sciences, 56, 374–399.Find this resource:
Wheeler, M. C., & Hendon, H. H. (2004). An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly Weather Review, 132, 1917–1932.Find this resource:
Williams, C. J. R., Kniveton, D. R., & Layberry, R. (2008). Influence of South Atlantic sea surface temperatures on rainfall variability and extremes over southern Africa. Journal of Climate, 21, 6498–6520.Find this resource:
Winstanley, D. (1973). Recent rainfall trends in Africa, the Middle East and India. Nature, 243, 464–465.Find this resource:
Zhang, C. (2006) Madden-Julian Oscillation. Reviews of Geophysics, 432, RG2003.Find this resource:
Zhang, C. (2013). Madden-Julian Oscillation: Bridging weather and climate. Bulletin of the American Meteorological Society, 94, 1849–1870.Find this resource:
Zhang, R., & Delworth, T. L. (2006). Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophysical Research Letters, 33, Article ID L17712.Find this resource:
Zhang, C., Woodworth, P., & Gu, G. (2006). The seasonal cycle in the lower troposphere over West Africa from sounding observations. Quarterly Journal of the Royal Meteorological Society, 132, 2559–2582.Find this resource: