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Observations of atmospheric carbon dioxide (CO) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO can be analyzed and modeled by geostatistical methods, and CO values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO, and does not consider temporal variation in the satellite-observed CO data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO data sets in China. Prediction of monthly CO using the spatiotemporal variogram model and space-time kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient ( r), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO than those from the spatial-only approach. [ABSTRACT FROM AUTHOR]

Satellite observations of atmospheric CO are able to truly capture the variation of global and regional CO concentration. The model simulations based on atmospheric transport models can also assess variations of atmospheric CO concentrations in a continuous space and time, which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO in a global scale. Satellite observations and model simulations of CO offer us two different approaches to understand the atmospheric CO. However, the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO. In this study, we compared and assessed the spatio-temporal variation of atmospheric CO using two datasets of the column-averaged dry air mole fractions of atmospheric CO (XCO) in a year from June 2009 to May 2010, respectively from GOSAT retrievals (V02.xx) and from Goddard Earth Observing System-Chemistry (GEOS-Chem), which is a global 3-D chemistry transport model. In addition to the global comparison, we further compared and analyzed the difference of CO between the China land region and the United States (US) land region from two datasets, and demonstrated the reasonability and uncertainty of satellite observations and model simulations. The results show that the XCO retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average, which is close to the validation conclusion for GOSAT by ground measures. This difference of XCO between the two datasets, however, changes with the different regions. In China land region, the difference is large, from 0.6 to 5.6 ppm, whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region. The goodness of fit test between the two datasets is 0.81 in the US land region, which is higher than that in the global land region (0.67) and China land region (0.68). The analysis results further indicate that the inconsistency of CO concentration between satellite observations and model simulations in China is larger than that in the US and the globe. This inconsistency is related to the GOSAT retrieval error of CO caused by the interference among input parameters of satellite retrieval algorithm, and the uncertainty of driving parameters in GEOS-Chem model. [ABSTRACT FROM AUTHOR]

Spatiotemporal patterns of column-averaged dry air mole fraction of CO2 (XCO2) have not been well characterized on a regional scale due to limitations in data availability and precision. This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset (2009–2016) derived from the Greenhouse gases Observing SATellite (GOSAT). XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model. The following results are found: Firstly, the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn. Secondly, on average, XCO2 increases by 2.08 ppm every year from 2010 to 2015, with a sharp increase of 2.6 ppm in 2013. Lastly, in the analysis of three typical regions, the GOSAT XCO2 time series is in better agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region (the least difference with bias 0.65±0.78 ppm), compared with the northern urban agglomeration region (−1.3±1.2 ppm) and the northeastern forest region (−1.4±1.4 ppm). The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data. [ABSTRACT FROM AUTHOR]