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Journal Article

Contrasting effects of CO2 fertilisation, land-use change and warming on seasonal amplitude of northern hemisphere CO2 exchange

MPS-Authors
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Rödenbeck,  Christian
Inverse Data-driven Estimation, Dr. C. Rödenbeck, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource

http://dx.doi.org/10.5194/acp-19-12361-2019
(Publisher version)

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BGC3043.pdf
(Publisher version), 2MB

Supplementary Material (public)

BGC3043s1.pdf
(Supplementary material), 3MB

Citation

Bastos, A., Ciais, P., Chevallier, F., Rödenbeck, C., Ballantyne, A. P., Maignan, F., et al. (2019). Contrasting effects of CO2 fertilisation, land-use change and warming on seasonal amplitude of northern hemisphere CO2 exchange. Atmospheric Chemistry and Physics, 19(19), 12361-12375. doi:10.5194/acp-19-12361-2019.


Cite as: https://hdl.handle.net/21.11116/0000-0003-46B2-5
Abstract
Continuous atmospheric CO2 monitoring data indicate an increase in seasonal-cycle amplitude (SCA) of CO2 exchange in northern high latitudes. The major drivers of enhanced SCA remain unclear and intensely debated with land-use change, CO2 fertilization and warming identified as likely contributors. We integrated CO2-flux data from two atmospheric inversions (consistent with atmospheric records) and from and 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCA of CO2-fluxes for 1980−2015. The LSMs generally reproduce the latitudinal increase in SCA trends within the inversions range. Inversions and LSMs attribute SCA increase to boreal Asia and Europe due to enhanced vegetation productivity (in LSMs) and point to contrasting effects of CO2 fertilisation (positive) and warming (negative) on SCA. Our results do not support land-use change as a key contributor to the increase in SCA. The sensitivity of simulated microbial respiration to temperature in LSMs explained biases in SCA trends, which suggests SCA could help to constrain model turnover times.