An analysis of the global spatial variability of column-averaged CO₂ from SCIAMACHY and its implications for CO₂ sources and sinks

Abstract

Satellite observations of carbon dioxide (CO₂) are important because of their potential for improving the scientific understanding of global carbon cycle processes and budgets. We present an analysis of the column-averaged dry air mole fractions of CO₂ (denoted XCO₂) of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) retrievals, which were derived from a satellite instrument with relatively long-term records (2003–2009) and with measurements sensitive to the near surface. The spatial-temporal distributions of remotely sensed XCO₂ have significant spatial heterogeneity with about 6–8% variations (367–397 ppm) during 2003–2009, challenging the traditional view that the spatial heterogeneity of atmospheric CO₂ is not significant enough (<4%) to have any large effect on terrestrial ecosystem carbon cycles. By comparison with surface measurements from the NOAA Earth System Research Laboratory (ESRL) GLOBALVIEW network, significant statistical relationships between XCO₂ and surface CO₂ were found for major ecosystems, with the exception of tropical forest. In addition, when compared with a simulated terrestrial carbon uptake from the Integrated Biosphere Simulator (IBIS) and the Emissions Database for Global Atmospheric Research (EDGAR) carbon emission inventory, the latitudinal gradient of XCO₂ seasonal amplitude was influenced by the combined effect of terrestrial carbon uptake, carbon emission, and atmospheric transport, suggesting no direct implications for terrestrial carbon sinks. From the investigation of the growth rate of XCO₂ we found that the increase of CO₂ concentration was dominated by temperature in the northern hemisphere (20–90°N) and by precipitation in the southern hemisphere (20–90°S), with the major contribution to global average occurring in the northern hemisphere. These findings indicated that the satellite measurements of atmospheric CO₂ improve not only the estimations of atmospheric inversion, but also the understanding of the terrestrial ecosystem carbon dynamics and its feedback to atmospheric CO₂.

Acknowledgements

We are grateful to the SCIAMACHY teams for the availability of SCIAMACHY WFM-DOAS data and thank the anonymous reviewers for their helpful contribution to this study. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government

Funding

Funding support was partially from the NSF China [Y411391001]; the NSF China Major Programme [61190114] and [41171324]; the State Key Fundamental Science Funds of China [2011CB302705], [2010CB950702], and [2010CB428503]; and the Project Funded by the Priority Academic Programme Development of Jiangsu Higher Education Institutions, Zhejiang province key science and technology innovation team [2010R50030].