Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China
Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in C...
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Elsevier
2022-03-01
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| Series: | Geoscience Frontiers |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987121001894 |
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| author | Md. Arfan Ali Muhammad Bilal Yu Wang Zhongfeng Qiu Janet E. Nichol Gerrit de Leeuw Song Ke Alaa Mhawish Mansour Almazroui Usman Mazhar Birhanu Asmerom Habtemicheal M. Nazrul Islam |
| author_facet | Md. Arfan Ali Muhammad Bilal Yu Wang Zhongfeng Qiu Janet E. Nichol Gerrit de Leeuw Song Ke Alaa Mhawish Mansour Almazroui Usman Mazhar Birhanu Asmerom Habtemicheal M. Nazrul Islam |
| author_sort | Md. Arfan Ali |
| collection | DOAJ |
| description | Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in China. To do this, AOD (550 nm) values from 2000 to 2014 were obtained from the Coupled Model Inter-comparison Project (CIMP6), the second version of Modern-Era Retrospective analysis for Research, and Applications (MERRA-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS; flying on the Terra satellite) combined Dark Target and Deep Blue (DTB) aerosol product. We used the Terra-MODIS DTB AOD (hereafter MODIS DTB AOD) as a standard to evaluate CMIP6 Ensemble AOD (hereafter CMIP6 AOD) and MERRA-2 reanalysis AOD (hereafter MERRA-2 AOD). Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD, than between CMIP6 and MODIS DTB AOD, in most regions of China, at both annual and seasonal scales. However, significant under- and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD. The long-term (2000–2014) MODIS DTB AOD distributions show the highest AOD over the North China Plain (0.71) followed by Central China (0.69), Yangtse River Delta (0.67), Sichuan Basin (0.64), and Pearl River Delta (0.54) regions. The lowest AOD values were recorded over the Tibetan Plateau (0.13 ± 0.01) followed by Qinghai (0.19 ± 0.03) and the Gobi Desert (0.21 ± 0.03). Large amounts of sand and dust particles emitted from natural sources (the Taklamakan and Gobi Deserts) may result in higher AOD in spring compared to summer, autumn, and winter. Trends were also calculated for 2000–2005, for 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan or FYP), and for 2011–2014 (during the 12th FYP). An increasing trend in MODIS DTB AOD was observed throughout the country during 2000–2014. The uncontrolled industrialization, urbanization, and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD. Finally, China's air pollution control policies helped to reduce AOD in most regions of the country; this was more evident during the 12th FYP period (2011–2014) than during the 11th FYP period (2006–2010). Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality. |
| first_indexed | 2024-03-12T08:24:53Z |
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| spelling | doaj.art-9e6a08896ef84721888269708d99b69c2023-09-02T18:08:55ZengElsevierGeoscience Frontiers1674-98712022-03-01132101325Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over ChinaMd. Arfan Ali0Muhammad Bilal1Yu Wang2Zhongfeng Qiu3Janet E. Nichol4Gerrit de Leeuw5Song Ke6Alaa Mhawish7Mansour Almazroui8Usman Mazhar9Birhanu Asmerom Habtemicheal10M. Nazrul Islam11Lab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaLab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaLab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaLab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China; Corresponding author.Department of Geography, School of Global Studies, University of Sussex, Brighton BN19RH, UKRoyal Netherlands Meteorological Institute (KNMI), R & D Satellite Observations, 3730AE De Bilt, The Netherlands; School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China; Aerospace Information Research Institute, Chinese Academy of Sciences (AirCAS), No. 20 Datun Road, Chaoyang District, Beijing 100101, China; School of Environment Science and Spatial Informatics, University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaGeological Survey of Jiangsu Province, Nanjing 210018, ChinaLab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCenter of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UKSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China; Department of Physics, Wollo University, Dessie P.O. Box 1145, EthiopiaCenter of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaRapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in China. To do this, AOD (550 nm) values from 2000 to 2014 were obtained from the Coupled Model Inter-comparison Project (CIMP6), the second version of Modern-Era Retrospective analysis for Research, and Applications (MERRA-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS; flying on the Terra satellite) combined Dark Target and Deep Blue (DTB) aerosol product. We used the Terra-MODIS DTB AOD (hereafter MODIS DTB AOD) as a standard to evaluate CMIP6 Ensemble AOD (hereafter CMIP6 AOD) and MERRA-2 reanalysis AOD (hereafter MERRA-2 AOD). Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD, than between CMIP6 and MODIS DTB AOD, in most regions of China, at both annual and seasonal scales. However, significant under- and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD. The long-term (2000–2014) MODIS DTB AOD distributions show the highest AOD over the North China Plain (0.71) followed by Central China (0.69), Yangtse River Delta (0.67), Sichuan Basin (0.64), and Pearl River Delta (0.54) regions. The lowest AOD values were recorded over the Tibetan Plateau (0.13 ± 0.01) followed by Qinghai (0.19 ± 0.03) and the Gobi Desert (0.21 ± 0.03). Large amounts of sand and dust particles emitted from natural sources (the Taklamakan and Gobi Deserts) may result in higher AOD in spring compared to summer, autumn, and winter. Trends were also calculated for 2000–2005, for 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan or FYP), and for 2011–2014 (during the 12th FYP). An increasing trend in MODIS DTB AOD was observed throughout the country during 2000–2014. The uncontrolled industrialization, urbanization, and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD. Finally, China's air pollution control policies helped to reduce AOD in most regions of the country; this was more evident during the 12th FYP period (2011–2014) than during the 11th FYP period (2006–2010). Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality.http://www.sciencedirect.com/science/article/pii/S1674987121001894Aerosol Optical DepthAir pollutionMERRA-2MODISAir quality |
| spellingShingle | Md. Arfan Ali Muhammad Bilal Yu Wang Zhongfeng Qiu Janet E. Nichol Gerrit de Leeuw Song Ke Alaa Mhawish Mansour Almazroui Usman Mazhar Birhanu Asmerom Habtemicheal M. Nazrul Islam Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China Geoscience Frontiers Aerosol Optical Depth Air pollution MERRA-2 MODIS Air quality |
| title | Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China |
| title_full | Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China |
| title_fullStr | Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China |
| title_full_unstemmed | Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China |
| title_short | Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China |
| title_sort | evaluation and comparison of cmip6 models and merra 2 reanalysis aod against satellite observations from 2000 to 2014 over china |
| topic | Aerosol Optical Depth Air pollution MERRA-2 MODIS Air quality |
| url | http://www.sciencedirect.com/science/article/pii/S1674987121001894 |
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