Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003–2016
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. XC...
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Veröffentlicht in: | Atmospheric chemistry and physics 2018-12, Vol.18 (23), p.17355-17370 |
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Sprache: | eng |
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Zusammenfassung: | 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 %. |
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ISSN: | 1680-7324 1680-7316 1680-7324 |
DOI: | 10.5194/acp-18-17355-2018 |