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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]

The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37-42° N segmented into 8 cells in a grid of 5° from west to east (80-120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and builtup areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7-1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0-1.6 ppm) with a highbrightness surface from the pairwise comparison results of XCO2 retrievals. (2) Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2), the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3) Viewing attributions of XCO2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO2 emissions, which implies that XCO2 from satellite observations could be reliably applied in the assessment of atmospheric CO2 enhancements induced by anthropogenic CO2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo. [ABSTRACT FROM AUTHOR]

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale. [ABSTRACT FROM AUTHOR]