Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution
- 1Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
- 2University of Chinese Academy of Sciences, Beijing 100049, China
- 3Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
- 4The Center for Climate Change and Environmental Policy, Chinese Academy for Environmental Planning, Ministry of Environmental Protection, Beijing 100012, China
- 5Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
Abstract. 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 built-up 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 high-brightness 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.