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The Global Spatiotemporal Distribution of the Mid-Tropospheric CO2 Concentration and Analysis of the Controlling Factors, 2019


Cao, L., Chen, X., Zhang, C., Kurban, A., Qian, J., Pan, T., Yin, Z., Qin, X., Ochege, F.U., De Maeyer, P.


Remote Sensing 11 :94
DOI: 10.3390/rs11010094

Abstract

The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO2 globally. In this paper, we use the AIRS CO2 product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO2 and the controlling factors using linear regression, empirical orthogonalfunctions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO2 concentrations (20S–5N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO2 emission areas. The areas with high mid-tropospheric CO2 concentrations (30N–70N) (382.1 ppm) are formed due to high CO2 emissions. The global mid-tropospheric CO2 concentrations increased gradually (the annual average rate of increase in CO2 concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO2 and related mechanisms.

Keywords

mid-tropospheric CO2; AIRS; spatiotemporal distribution; station observations; atmospheric circulation 

Citation

Cao, L., Chen, X., Zhang, C., Kurban, A., Qian, J., Pan, T., Yin, Z., Qin, X., Ochege, F.U., De Maeyer, P., (2019) The Global Spatiotemporal Distribution of the Mid-Tropospheric CO2 Concentration and Analysis of the Controlling Factors. Remote Sensing 11 :94 , DOI: 10.3390/rs11010094

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This material is based upon work supported by the National Science Foundation under Cooperative Agreement #DEB-1637686. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necesarily reflect the views of the National Science Foundation.

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