Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness

Abstract

As a crucial aspect of the climate system, changes in Africa’s atmospheric layer thickness, i.e., the vertical distance spanning a specific layer of the Earth’s atmosphere, could impact its weather, air quality, and ecosystem. This study did not only examine the trends but also applied a deep autoencoder artificial neural network to detect years with significant anomalies in the thickness of Africa’s atmosphere over a given homogeneous region (derived with the rotated principal component analysis) and examine the fingerprint of global warming on the thickness changes. The broader implication of this study is to further categorize regions in Africa that have experienced significant changes in their climate system. The study reveals an upward trend in thickness between 1000 and 850 hPa across substantial parts of Africa since 1950. Notably, the spatial breadth of this rise peaks during the boreal summer. Correlation analysis, further supported by the deep autoencoder neural network, suggests the fingerprint of global warming signals on the increasing vertical extent of Africa’s atmosphere and is more pronounced (since the 2000s) in the south-central regions of Africa (specifically the Congo Basin). Additionally, the thickness over the Sahel and Sahara Desert sees no significant increase during the austral summer, resulting from the counteracting effect of the positive North Atlantic Oscillation, which prompts colder conditions over the northern parts of Africa. As the atmospheric layer thickness impacts the temperature and moisture distribution of the layer, our study contributes to its historical assessment for a sustainable ecosystem.