Aerosols, atmospheric transmissivity and hydrological modelling of climatic change over Africa south of the equator

Tyson, Peter; Gasse, Françoise; Bouchet, L.; D'Abreton, P. C.

doi:10.1002/(sici)1097-0088(199712)17:15<1651::aid-joc216>3.0.co;2-i

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…In addition, NCEP reanalysis data exhibited a decrease in annual mean solar and net radiation from south to north, agreeing with our results (Table 2). Our mean estimate for Q (161 W m 22 ) was very similar to Q suggested by NCEP reanalysis data (160 W m 22 ; calculated from net incoming long-wave and solar radiation for the same time period, combined with our outgoing long-wave radiation) and that found by simulation for lake-wide Q at Lake Tanganyika (159 W m 22 ) by Tyson et al (1997).…”

Section: Resultssupporting

confidence: 86%

Differential cooling drives large‐scale convective circulation in Lake Tanganyika

Verburg

Antenucci

Hecky

2011

Limnology & Oceanography

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Field data, scaling analysis, and three-dimensional numerical experiments demonstrate that the dominant large-scale circulation pattern during the southeast trade winds in Lake Tanganyika (East Africa) is a downwind flow in the metalimnion, with a returning southward upwind flow in the upper region of the epilimnion. This is in the opposite direction to all prior literature available on the lake, which has assumed that the southeast trade winds must drive a northward surface flow due to momentum induced by the wind. The gradient in wind speed and humidity along the length of the lake and the resulting gradient in heat exchange with the atmosphere result in warm water accumulating at the northern end of the lake. The north basin of Lake Tanganyika, thus, acts as a heat sink while the south basin is a net source of heat to the atmosphere. The resulting variation in buoyancy flux is strong enough to drive a convective flow in the upper layer in the opposite direction to the wind. A large-scale convective circulation results in an annual exchange of 6.2 3 10 12 m 3 of water and an annual net transport of heat (averaging 0.2 terawatt) from the northern to the southern region of the lake. Currents in the upper 100-m layer, with a mean flow of 0.2 Sverdrup and flow speed of 0.08 m s 21 , can be described by the superposition of a parallel convective flow with the internal seiche motion.

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Section: Resultssupporting

confidence: 86%

Differential cooling drives large‐scale convective circulation in Lake Tanganyika

Verburg

Antenucci

Hecky

2011

Limnology & Oceanography

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“…Figure 4c shows the relationship of τ with an elevation, which results in r 2 = 0.26 for the NH. τ tends to increase with an increase in elevation, due to the difference in the optical depth [20,21,78]. Figure 4d shows the relationship of τ with DTR, which showed an increasing trend for both hemispheres and ranged between 3 °C to 17 °C.…”

Section: R Peer Review 9 Of 17mentioning

confidence: 97%

“…According to the hypothesis, higher average transmissivity leads to more in-coming shortwave radiation, hence less cloud cover [33]. However, the results in this study did not replicate this hypothesis, which may be attributed to the fact that the temperature does not hold strong control over τ. Elevation determines the optical path length, so it may be one of the crucial factors that alters τ [20,21]. τ is easily expressed as a function of optical thickness, which is a measure of the extinction properties (scattering and absorption) of the light path.…”

Section: Transmissivity and Relationship With Other Factorsmentioning

confidence: 99%

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Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

et al. 2021

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Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

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“…At this last site, a water and energy balance model has simulated a similar decrease in P ann of ca. 275 mm during the LGM but, according to Tyson et al (1997), this value must be considered as a minimal estimate, as decreases in P ann may be substantially amplified when including empirical changes in atmospheric transmission coefficient, whereas large changes in cloud cover and air humidity not modify these trends. For this period, our reconstructions of the decrease in P ann are clearly over-estimated when compared to results obtained in West equatorial Africa and in East Africa.…”

Section: P Ann Reconstructions At 21 000 Cal Yr Bpmentioning

confidence: 99%

Pollen, vegetation change and climate at Lake Barombi Mbo (Cameroon) during the last ca. 33 000 cal yr BP: a numerical approach

2012

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Abstract. This paper presents quantitative reconstructions of vegetation and climate along the pollen sequence of Lake Barombi Mbo, southwestern Cameroon (4 • 39 45.75 N, 9 • 23 51.63 E, 303 m a.s.l.) during the last 33 000 cal yr BP, improving previous empirical interpretations. The biomisation method was applied to reconstruct potential biomes and forest successional stages. Mean annual precipitation, mean annual potential evapotranspiration and an index of moisture availability were reconstructed using modern analogues and an artificial neural network technique. The results show a dense forested environment around Lake Barombi Mbo of mixed evergreen/semi-deciduous type during the most humid phases (highest precipitation and lowest evapotranspiration), but with a more pronounced semi-deciduous type from ca. 6500 cal yr BP to the present day, related to increased seasonality. This forest displays a mature character until ca. 2800 cal yr BP, then becomes of secondary type during the last millennium, probably due to increased human activity. Two episodes of forest fragmentation are shown, which are synchronous with the lowest reconstructed precipitation and highest potential evapotranspiration values. The first of these occurs during the LGM, and the second one from ca. 3000 to ca. 1200 cal yr BP, mainly linked to high precipitation seasonality. Savanna were, however, never extensive within the Barombi Mbo basin, existing instead inside the forest in form of savanna patches. The climate reconstructions at Lake Barombi Mbo suggest that the artificial neural networks technique would be more reliable in this region, although the annual precipitation values are likely under-estimated through the whole sequence.

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Aerosols, atmospheric transmissivity and hydrological modelling of climatic change over Africa south of the equator

Cited by 9 publications

References 36 publications

Differential cooling drives large‐scale convective circulation in Lake Tanganyika

Differential cooling drives large‐scale convective circulation in Lake Tanganyika

Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

Pollen, vegetation change and climate at Lake Barombi Mbo (Cameroon) during the last ca. 33 000 cal yr BP: a numerical approach

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