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Surface Temperature Variations in East Africa and Possible Causes

Domaine:

climate

Type de record:

dataset
Créateur:
JohWilRic
Éditeur:
Ame
Hôte:
Abstract Surface temperatures have been observed in East Africa for more than 100 yr, but heretofore have not been subject to a rigorous climate analysis. To pursue this goal monthly averages of maximum (TMax), minimum (TMin), and mean (TMean) temperatures were obtained for Kenya and Tanzania from several sources. After the data were organized into time series for specific sites (60 in Kenya and 58 in Tanzania), the series were adjusted for break points and merged into individual gridcell squares of 1.25°, 2.5°, and 5.0°. Results for the most data-rich 5° cell, which includes Nairobi, Mount Kilimanjaro, and Mount Kenya, indicate that since 1905, and even recently, the trend of TMax is not significantly different from zero. However, TMin results suggest an accelerating temperature rise. Uncertainty estimates indicate that the trend of the difference time series (TMax − TMin) is significantly less than zero for 1946–2004, the period with the highest density of observations. This trend difference continues in the most recent period (1979–2004), in contrast with findings in recent periods for global datasets, which generally have sparse coverage of East Africa. The differences between TMax and TMin trends, especially recently, may reflect a response to complex changes in the boundary layer dynamics; TMax represents the significantly greater daytime vertical connection to the deep atmosphere, whereas TMin often represents only a shallow layer whose temperature is more dependent on the turbulent state than on the temperature aloft. Because the turbulent state in the stable boundary layer is highly dependent on local land use and perhaps locally produced aerosols, the significant human development of the surface may be responsible for the rising TMin while having little impact on TMax in East Africa. This indicates that time series of TMax and TMin should become separate variables in the study of long-term changes.

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