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Mitigation of ozone damage to the world’s land ecosystems by source sector

Abstract

Surface ozone damages photosynthesis and reduces the ability of land ecosystems to assimilate carbon from the atmosphere thereby further increasing global warming1,2. Ozone is not emitted directly but formed in the atmosphere during complex chemical reactions of precursors, carbon monoxide, methane, non-methane volatile organic compounds and nitrogen oxides, in sunlight. These ozone precursors are emitted from a wide range of anthropogenic activities. Reductions in ozone precursor emissions are needed to mitigate ozone vegetation damage but it is unclear which are the most effective source sectors to target. Here, we apply a global Earth system model to compare the benefits to gross primary productivity of stringent 50% emission reductions in the seven largest anthropogenic ozone source sectors. Deep cuts in air pollutant emissions from road transportation and the energy sector are the most effective mitigation measures for ozone-induced gross primary productivity losses in Eastern China, Eastern United States, Europe and globally. Our results suggest that mitigation of ozone vegetation damage is a unique opportunity to contribute to negative carbon emissions, offering a natural climate solution that links fossil fuel emission abatement, air quality and climate. However, achieving these benefits requires ambitious mitigation pathways that tackle multiple source sectors.

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Fig. 1: Annual average GPP losses due to O3 damage.
Fig. 2: Annual average surface O3 concentration decreases in ppbv due to 50% source sector emission reduction relative to CTRL.
Fig. 3: Annual average GPP increases due to 50% source sector emission reduction relative to CTRL.

Data availability

Model output data relating to this study not presented in the manuscript are available from the corresponding author on request.

Code availability

The YIBs model was developed by X.Y. and N.U. and is available at https://github.com/YIBS01/YIBS_site. The source code for the frozen CMIP5/AR5 version of the GISS ModelE2 can be obtained from NASA GISS (https://www.giss.nasa.gov/tools/modelE/).

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Acknowledgements

N.U. acknowledges funding support from the University of Exeter. X.Y. acknowledges funding support from the National Natural Science Foundation of China (grant no. 41975155) and the National Key Research and Development Program of China (grant no. SQ2019YFA060013). This project was supported by the ISCA High Performance Computing facility at the University of Exeter. The authors thank K.J. van Groenigen and S. Sitch for helpful discussions.

Author information

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Authors

Contributions

N.U. conceived the project. N.U. and Y.Z. designed the research. Y.Z. performed the simulations. X.Y. calibrated, validated and evaluated the land carbon fluxes and O3 vegetation damage in YIBs. K.L.H. developed and evaluated the dynamic CH4 simulation. N.U. and Y.Z. analysed model output. N.U. wrote the paper with all authors providing input.

Corresponding author

Correspondence to Nadine Unger.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Ben Felzer, Jin Han Park and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended Data

Extended Data Fig. 1 Annual average surface O3 concentration in ppbv in the CTRL simulation.

Results shown are the decadal average for model run years 15–24. The 3 key regions are marked with black boxes: Eastern US, Europe and Eastern China.

Extended Data Fig. 2 Comparison of June-July-August (JJA) average modelled and observed surface O3 in ppbv.

Observed surface O3 is from a global gridded dataset compiled by Sofen et al40. The monitoring networks are predominantly in the US and Europe. Observations are climatological averages 2001–2010. The modelled values are from the CTRL simulation. The correlation coefficient (R) and normalized mean bias (NMB) are shown in the figure panel where N=130 is the number of grid cells used for the comparison statistics.

Supplementary information

Supplementary Information

Supplementary Tables 1–6.

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Unger, N., Zheng, Y., Yue, X. et al. Mitigation of ozone damage to the world’s land ecosystems by source sector. Nat. Clim. Chang. 10, 134–137 (2020). https://doi.org/10.1038/s41558-019-0678-3

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