Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness
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 autoen...
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ftdoajarticles:oai:doaj.org/article:ab897d928a6046db8b204626ade444db 2024-02-11T10:06:18+01:00 Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness Chibuike Chiedozie Ibebuchi Itohan-Osa Abu Clement Nyamekye Emmanuel Agyapong Linda Boamah 2023-12-01T00:00:00Z https://doi.org/10.3390/su16010256 https://doaj.org/article/ab897d928a6046db8b204626ade444db EN eng MDPI AG https://www.mdpi.com/2071-1050/16/1/256 https://doaj.org/toc/2071-1050 doi:10.3390/su16010256 2071-1050 https://doaj.org/article/ab897d928a6046db8b204626ade444db Sustainability, Vol 16, Iss 1, p 256 (2023) Africa atmospheric layer thickness trend deep autoencoders global warming North Atlantic Oscillation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 article 2023 ftdoajarticles https://doi.org/10.3390/su16010256 2024-01-14T01:38:42Z 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. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Austral Sustainability 16 1 256 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Africa atmospheric layer thickness trend deep autoencoders global warming North Atlantic Oscillation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
spellingShingle |
Africa atmospheric layer thickness trend deep autoencoders global warming North Atlantic Oscillation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Chibuike Chiedozie Ibebuchi Itohan-Osa Abu Clement Nyamekye Emmanuel Agyapong Linda Boamah Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness |
topic_facet |
Africa atmospheric layer thickness trend deep autoencoders global warming North Atlantic Oscillation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Chibuike Chiedozie Ibebuchi Itohan-Osa Abu Clement Nyamekye Emmanuel Agyapong Linda Boamah |
author_facet |
Chibuike Chiedozie Ibebuchi Itohan-Osa Abu Clement Nyamekye Emmanuel Agyapong Linda Boamah |
author_sort |
Chibuike Chiedozie Ibebuchi |
title |
Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness |
title_short |
Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness |
title_full |
Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness |
title_fullStr |
Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness |
title_full_unstemmed |
Utilizing Machine Learning to Examine the Spatiotemporal Changes in Africa’s Partial Atmospheric Layer Thickness |
title_sort |
utilizing machine learning to examine the spatiotemporal changes in africa’s partial atmospheric layer thickness |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/su16010256 https://doaj.org/article/ab897d928a6046db8b204626ade444db |
geographic |
Austral |
geographic_facet |
Austral |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Sustainability, Vol 16, Iss 1, p 256 (2023) |
op_relation |
https://www.mdpi.com/2071-1050/16/1/256 https://doaj.org/toc/2071-1050 doi:10.3390/su16010256 2071-1050 https://doaj.org/article/ab897d928a6046db8b204626ade444db |
op_doi |
https://doi.org/10.3390/su16010256 |
container_title |
Sustainability |
container_volume |
16 |
container_issue |
1 |
container_start_page |
256 |
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1790603940853710848 |