University of Leicester
acp-18-17355-2018.pdf (3.08 MB)

Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016

Download (3.08 MB)
journal contribution
posted on 2019-08-19, 09:24 authored by M Buchwitz, M Reuter, O Schneising, S Noel, B Gier, H Bovensmann, JP Burrows, H Boesch, J Anand, RJ Parker, P Somkuti, RG Detmers, OP Hasekamp, I Aben, A Butz, A Kuze, H Suto, Y Yoshida, D Crisp, C O'Dell
The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined from satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, for the years 2003 to 2016. The XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0±0.3 ppm year−1; R: 0.82). This new and independent data set confirms record-large growth rates of around 3 ppm year−1 in 2015 and 2016, which are attributed to the 2015–2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric CO2 growth rate. Our analysis shows that the ENSO impact on CO2 growth rate variations dominates that of human emissions throughout the period 2003–2016 but in particular during the period 2010–2016 due to strong La Niña and El Niño events. Using the derived growth rates and their uncertainties, we estimate the probability that the impact of ENSO on the variability is larger than the impact of human emissions to be 63 % for the time period 2003–2016. If the time period is restricted to 2010–2016, this probability increases to 94 %.


This study has been funded in part by the European Space Agency (ESA) (via the GHG-CCI project of ESA's Climate Change Initiative; CCI,, last access: 10 October 2017), by the European Union (EU) (via the Copernicus Climate Change Service (C3S,, last access: 11 January 2018) managed by the European Centre for Medium-range Weather Forecasts, ECMWF) and by the state of Bremen and the University of Bremen. The University of Leicester GOSAT retrievals used the ALICE High Performance Computing Facility at the University of Leicester. We thank ESA/DLR for providing us with SCIAMACHY level 1 data products and JAXA for GOSAT level 1B data. We also thank ESA for making these GOSAT products available via the ESA Third Party Mission archive. We thank NIES for the operational GOSAT XCO2 level 2 product and the NASA/ACOS team for the GOSAT ACOS level 2 XCO2 product. We also thank NOAA for the global CO2 growth rates (; last access: 24 November 2017) and for the Mauna Loa (MLO) CO2 growth rates (; last access: 9 August 2018). The fossil fuel and industry CO2 emissions have been obtained from the Global Carbon Project website (; last access: 20 November 2017). The Southern Oscillation Index (SOI) data have been obtained from NOAA (; last access: 20 November 2017). The Oceanic Niño Index (ONI) data have also been obtained from NOAA (, last access: 20 November 2017). The CAMS CO2 reanalysis data set has been obtained from (last access: 13 November 2018).



Atmospheric Chemistry and Physics, 2018, 18, 17355–17370

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Physics and Astronomy

Published in

Atmospheric Chemistry and Physics


European Geosciences Union (EGU), Copernicus Publications





Acceptance date


Copyright date


Available date


Publisher version