Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution

• Published in: Atmospheric measurement techniques 2018-03, Vol.11 (3), p.1251-1272
• Main Authors: Bie, Nian, Lei, Liping, Zeng, ZhaoCheng, Cai, Bofeng, Yang, Shaoyuan, He, Zhonghua, Wu, Changjiang, Nassar, Ray
• Format: Article
• Language: English
• Subjects: Aerosols; Agricultural land; Air pollution; Albedo; Albedo (solar); Algorithms; Anthropogenic factors; Atmospheric particulates; Carbon; Carbon dioxide; Carbon dioxide atmospheric concentrations; Carbon dioxide emissions; Cells; Chemical transport; Computer simulation; Datasets; Deserts; Dry air; Dust storms; Environmental protection; Fourier transforms; Gases; Grasslands; Greenhouse effect; Greenhouse gases; Intercomparison; Megacities; Physics; Regional analysis; Satellite observation; Satellites; Simulation; Three dimensional models; Uncertainty
• ISSN: 1867-1381; 1867-8548
• DOI: 10.5194/amt-11-1251-2018

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 GOSATXCO2 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 ofXCO2 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 anthropogenicCO2 emissions, which implies that XCO2 from satellite observations could be reliably applied in the assessment of atmospheric CO2 enhancements induced by anthropogenicCO2 emissions. The large inconsistency among the four algorithms presented in w