A Cluster of CO2 Change Characteristics with GOSAT Observations for Viewing the Spatial Pattern of CO2 Emission and Absorption

Satellite observations can be used to detect the changes of CO2 concentration at global and regional scales. With the column-averaged CO2 dry-air mole fraction (Xco(2)) data derived from satellite observations, the issue is how to extract and assess these changes, which are related to anthropogenic emissions and biosphere absorptions. We propose a k-means cluster analysis to extract the temporally changing features of Xco(2) in the Central-Eastern Asia using the data from 2009 to 2013 obtained by Greenhouse Gases Observing Satellite (GOSAT), and assess the effects of anthropogenic emissions and biosphere absorptions on CO2 changes combining with the data of emission and vegetation net primary production (NPP). As a result, 14 clusters, which are 14 types of Xco(2) seasonal changing patterns, are obtained in the study area by using the optimal clustering parameters. These clusters are generally in agreement with the spatial pattern of underlying anthropogenic emissions and vegetation absorptions. According to correlation analysis with emission and NPP, these 14 clusters are divided into three groups: strong emission, strong absorption, and a tendency of balancing between emission and absorption. The proposed clustering approach in this study provides us with a potential way to better understand how the seasonal changes of CO2 concentration depend on underlying anthropogenic emissions and vegetation absorptions.