Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis

The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of...

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Main Authors: Martins, Vitor S., Novo, Evlyn M.L.M., Lyapustin, Alexei, Aragão, Luiz E.O.C., Freitas, Saulo R., Barbosa, Claudio C.F.
Format: Text
Language:English
Published: Iowa State University Digital Repository 2018
Subjects:
Online Access:https://lib.dr.iastate.edu/abe_eng_pubs/1027
https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2312&context=abe_eng_pubs
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author Martins, Vitor S.
Novo, Evlyn M.L.M.
Lyapustin, Alexei
Aragão, Luiz E.O.C.
Freitas, Saulo R.
Barbosa, Claudio C.F.
author_facet Martins, Vitor S.
Novo, Evlyn M.L.M.
Lyapustin, Alexei
Aragão, Luiz E.O.C.
Freitas, Saulo R.
Barbosa, Claudio C.F.
author_sort Martins, Vitor S.
collection Digital Repository @ Iowa State University
description The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (>80%) throughout the year, while low cloud cover (0–20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon – unlike previous droughts. The water vapor presents high concentration values (4.0–5.0 g cm−2) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content (∼0.3 g/cm2) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25–35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.
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op_rights Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
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spelling ftiowastateuniv:oai:lib.dr.iastate.edu:abe_eng_pubs-2312 2025-01-16T18:39:19+00:00 Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis Martins, Vitor S. Novo, Evlyn M.L.M. Lyapustin, Alexei Aragão, Luiz E.O.C. Freitas, Saulo R. Barbosa, Claudio C.F. 2018-11-01T07:00:00Z application/pdf https://lib.dr.iastate.edu/abe_eng_pubs/1027 https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2312&context=abe_eng_pubs en eng Iowa State University Digital Repository https://lib.dr.iastate.edu/abe_eng_pubs/1027 https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2312&context=abe_eng_pubs Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. Agricultural and Biosystems Engineering Publications Aerosol Water vapor Ozone Cloud cover MODIS-MAIAC Climatology Atmospheric Sciences Bioresource and Agricultural Engineering Climate Environmental Monitoring text 2018 ftiowastateuniv 2021-08-28T22:48:44Z The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (>80%) throughout the year, while low cloud cover (0–20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon – unlike previous droughts. The water vapor presents high concentration values (4.0–5.0 g cm−2) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content (∼0.3 g/cm2) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25–35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations. Text Aerosol Robotic Network Digital Repository @ Iowa State University
spellingShingle Aerosol
Water vapor
Ozone
Cloud cover
MODIS-MAIAC
Climatology
Atmospheric Sciences
Bioresource and Agricultural Engineering
Climate
Environmental Monitoring
Martins, Vitor S.
Novo, Evlyn M.L.M.
Lyapustin, Alexei
Aragão, Luiz E.O.C.
Freitas, Saulo R.
Barbosa, Claudio C.F.
Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis
title Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis
title_full Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis
title_fullStr Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis
title_full_unstemmed Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis
title_short Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis
title_sort seasonal and interannual assessment of cloud cover and atmospheric constituents across the amazon (2000–2015): insights for remote sensing and climate analysis
topic Aerosol
Water vapor
Ozone
Cloud cover
MODIS-MAIAC
Climatology
Atmospheric Sciences
Bioresource and Agricultural Engineering
Climate
Environmental Monitoring
topic_facet Aerosol
Water vapor
Ozone
Cloud cover
MODIS-MAIAC
Climatology
Atmospheric Sciences
Bioresource and Agricultural Engineering
Climate
Environmental Monitoring
url https://lib.dr.iastate.edu/abe_eng_pubs/1027
https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2312&context=abe_eng_pubs