Abstract
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 (Xco2) 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 Xco2 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 Xco2 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.
Highlights
The global carbon cycle has been changed by human activities since the beginning of the industrial era [1]
It is well known that CO2 is a long-lived greenhouse gas, and the gradients generated by local fluxes are relatively small compared with background concentrations [5]
A k-means cluster analysis method based on the temporally changing features of Xco2 was proposed for application to the gap-filled Atmospheric CO2 Observations from Space (ACOS) Xco2 dataset to view spatial pattern of CO2 emissions and absorption in Central-Eastern Asia. 14 clusters were obtained by optimizing the clustering results and evaluated using the characteristics of Xco2 variations combined with emissions data, net primary production (NPP)
Summary
The global carbon cycle has been changed by human activities since the beginning of the industrial era [1]. Anthropogenic emissions of CO2, especially that from burning fossil fuels, is considered to be a major cause of the continual increase of atmospheric carbon dioxide (CO2) concentrations [2,3]. This is the leading driving factor of climate change and global warming [4]. For a long past time, ground-based observations had been the only reliable way of obtaining stable, highly accurate data of CO2 concentrations in the atmosphere, which have helped us in understanding the global and latitudinal variations of atmospheric CO2 concentration [8,9]. The sparseness of current ground-based measurement stations [7,9,10] has been limiting our knowledge of the global carbon cycle [11]
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