Deriving Full‐Coverage and Fine‐Scale XCO<sub>2</sub> Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output
Changpei He, Mingrui Ji, Tao Li, Xinyi Liu, Dié Tang, Shifu Zhang, Yuzhou Luo, Michael L. Grieneisen, Zihang Zhou, Yu Zhan
Abstract
Abstract Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO 2 (XCO 2 ) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO 2 . We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals across China during 2015–2018, with cross‐validation R 2 = 0.95 and RMSE = 0.91 ppm. Based on the gap‐filled data set, the multiyear average XCO 2 was the highest in East China (405.71 ± 3.72 ppm) and the lowest in Northwest China (403.99 ± 3.47 ppm). At the national level, the multiyear seasonal XCO 2 varied from 402.54 ± 3.95 ppm in summer to 406.28 ± 3.19 ppm in spring. While the XCO 2 kept increasing, the rate of increase declined from 3.23 to 2.10 ppm/year. The machine learning approach is feasible for downscaling and calibrating the CarbonTracker XCO 2 data. The full‐coverage and fine‐scale XCO 2 data set is expected to advance our understanding of the carbon cycles.