How Much CO₂ Is Taken Up by the European Terrestrial Biosphere?

• Published in: Bulletin of the American Meteorological Society Vol. 98; no. 4; pp. 665 - 672
• Main Authors: Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Bovensmann, H., Burrows, J. P., Houweling, S., Liu, Y. Y., Nassar, R., Chevallier, F., Ciais, P., Marshall, J., Reichstein, M.
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.04.2017
• Subjects: Algorithms; Atmosphere; Bias; Biogeochemistry; Biomass; Biosphere; Carbon; Carbon dioxide; Carbon dioxide atmospheric concentrations; Carbon dioxide concentration; Climate change; Ecosystem assessment; Ecosystems; Estimates; Greenhouse gases; IN BOX INSIGHTS and INNOVATIONS; Measurement techniques; Physics; Stock assessment; Studies
• ISSN: 0003-0007; 1520-0477
• DOI: 10.1175/BAMS-D-15-00310.1

[...]conventional bottom-up estimates of surface carbon fluxes are obtained from field measurements-for example, employing the eddy covariance method and assessing ecosystem carbon stock change at biome-representative sites and subsequently scaled up to the entire region of interest. In 2002, near-infrared (NIR) satellite measurements of atmospheric CO2 concentrations became available [Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY); Burrows et al. 1995; Bovensmann et al. 1999] and together with follow-up satellite missions [Greenhouse Gases Observing Satellite (GOSAT), launched 2009 (Kuze et al. 2009), and Orbiting Carbon Observatory-2 (OCO-2), launched 2014 (Crisp et al. 2004)] a scientific community grew, aiming to use satellite data to further reduce the uncertainties of global and regional sources and sinks of CO2. [...]the large discrepancy between conventional bottom-up and in situ inversion estimates on the one hand and new evidence from different kinds of satellite measurements on the other hand is currently subject to intense discussions (e.g., Chevallier et al. 2014; Reuter et al. 2014; Houweling et al. 2015; Feng et al. 2016). Aligned with this, inverse modeling tools need to be optimized for the characteristics of the satellite data-for example, by minimizing model errors due to prescribed emissions, chemistry, or transport, by better accounting for error correlations of the retrievals, and/or by simultaneously fitting the parameters of a bias model (e.g., Basu et al. 2013; Reuter et al. 2014). FOR FURTHER READING M Reuter (1), M Buchwitz (2), M Hilker (3), J Heymann (4), H Bovensmann (5), J P Burrows (6), S Houweling (7), Y...