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    Annual Trend and Interactions between the Ozone Layer, Vegetation Cover, and Climate in Africa - Insights from 2003 to 2023 Reanalysis Data

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    Master degree in CLIMATE CHANGE Engineering (4.370Mb)
    Date
    2025-04-15
    Author
    MAPULENDE, Ricardo da Crescência
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    Abstract
    The present research examines the annual trends and interactions among the ozone layer, vegetation cover, and climate variables in Africa from 2003 to 2023 using reanalysis data. The aim was to illustrate the temporal and spatial variability of total column ozone (TCO), the normalized difference vegetation index (NDVI), temperature, and precipitation; analyze their yearly trends; and assess the statistical significance of various factors influencing their variability. Our methodology utilized descriptive statistics and trend analysis—specifically, the Mann-Kendall test, Sen's slope, and Buishand test—along with statistical modeling techniques, including multivariate linear regression, principal component analysis, and generalized additive models. The results revealed a widespread warming trend across the continent, with significant temperature increases noted in 59.21% of Africa, particularly after 2013. Precipitation trend results were more complex, reflecting both upward and downward changes that highlight regional climate variability. The Total Column Ozone (TCO) exhibited a generally positive trend, indicating a potential recovery of the ozone layer, likely linked to the enforcement of the Montreal Protocol. NDVI analysis revealed a varied pattern of vegetation changes, with both gains and losses observed throughout the continent. Statistical modeling results indicated significant correlations between climate factors and the target variables. Multivariate linear regression highlighted the roles of atmospheric dynamics and air quality in shaping regional temperatures. At the same time, principal component analysis (PCA) consolidated complex interactions, emphasizing the impacts of air quality, temperature, atmospheric composition, and vegetation. Generalized additive models (GAMs) elucidated non-linear relationships by identifying the influences of latitude, wind patterns, and greenhouse gases on temperature, precipitation, and ozone levels. These findings highlight the complex relationships between climate, vegetation, and ozone dynamics in Africa, offering valuable insights into climate modeling and future climate change projections. They also emphasize the necessity for continuous monitoring and improved modeling methods.
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    http://repository.pauwes-cop.net/handle/1/537
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