Analysis of the carbon dioxide concentration in the lowest atmospheric layers and the factors affecting China based on satellite observations

Abstract

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas contributing to global climate change. SCIAMACHY on board ENVISAT (launched in 2002) is the first satellite instrument to monitor the changes in CO₂ concentration in the lowest atmospheric layers. The temporal and spatial distribution of CO₂ (2003–2009) concentration based on SCIAMACHY over China is presented and discussed. It shows an annual increase and a seasonal cycle. The CO₂ annual growth rate was about 1.8 ppm year⁻¹, with the highest value being in spring and the lowest in autumn. The CO₂ concentration variation is determined by many complex factors. In this article, we analyse the important factors affecting CO₂ variations, with special emphasis on terrestrial ecosystems and energy consumption. Terrestrial ecosystems are an important sink in the global carbon cycle. The relationship between CO₂ concentration and Moderate Resolution Imaging Spectroradiometer (MODIS) net primary production (NPP) in 2008 is analysed. CO₂ concentration is inversely proportional to NPP both in regions with high-density vegetation and in deserts. The Yunnan province has the highest NPP value and the lowest CO₂ concentration, whereas the Takla Makan Desert has the lowest NPP value and the highest CO₂ concentration. Energy consumption is the main emission source of atmospheric CO₂. CO₂ emissions from energy consumption show a steady increase in China since 1980. China's CO₂ concentration variation shows a high correlation with energy consumption (coefficient of determination (R ²) > 0.8). The regions with high energy consumption are major industrial regions such as Shandong, Guangdong, Jiangsu, Zhejiang, Hebei, and Henan.

Acknowledgements

This research was supported by the National Science and Technology Supporting Projects of China (No. 2008BAH31B03, No. 2011BAH23B03). We thank the University of Bremen for providing us with the SCIAMACHY WFMD v2.1 CO₂ data product. We thank Prof. Arthur Cracknell for editing this article. We also thank the anonymous reviewers for their constructive comments and suggestions.