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As an important cause of global warming, CO 2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO 2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO 2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO 2 data as the accuracy control conditions to obtain high accuracy XCO 2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO 2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO 2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO 2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO 2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO 2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist.

Simulation and analysis of XCO2 in North China based on high accuracy surface modeling