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Isotopic constraint on the twentieth-century increase in tropospheric ozone

Nature volume 570, pages 224–227 (2019)Cite this article

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Abstract

Tropospheric ozone (O3) is a key component of air pollution and an important anthropogenic greenhouse gas1. During the twentieth century, the proliferation of the internal combustion engine, rapid industrialization and land-use change led to a global-scale increase in O3 concentrations2,3; however, the magnitude of this increase is uncertain. Atmospheric chemistry models typically predict4,5,6,7 an increase in the tropospheric O3 burden of between 25 and 50 per cent since 1900, whereas direct measurements made in the late nineteenth century indicate that surface O3 mixing ratios increased by up to 300 per cent8,9,10 over that time period. However, the accuracy and diagnostic power of these measurements remains controversial2. Here we use a record of the clumped-isotope composition of molecular oxygen (18O18O in O2) trapped in polar firn and ice from 1590 to 2016 ad, as well as atmospheric chemistry model simulations, to constrain changes in tropospheric O3 concentrations. We find that during the second half of the twentieth century, the proportion of 18O18O in O2 decreased by 0.03 ± 0.02 parts per thousand (95 per cent confidence interval) below its 1590–1958 ad mean, which implies that tropospheric O3 increased by less than 40 per cent during that time. These results corroborate model predictions of global-scale increases in surface pollution and vegetative stress caused by increasing anthropogenic emissions of O3 precursors4,5,11. We also estimate that the radiative forcing of tropospheric O3 since 1850 ad is probably less than +0.4 watts per square metre, consistent with results from recent climate modelling studies12.

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Fig. 1: Mean atmospheric Δ36 values from different archives.
Fig. 2: Measurement–model comparisons for increase in tropospheric O3 since 1850 ad.
Fig. 3: Zonal-mean change in O3 concentrations between 1850 and 2005 ad.

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Data availability

The isotopic data and main LOCK-IN firn data that support the findings of this study are available from the PANGAEA database (https://doi.pangaea.de/10.1594/PANGAEA.901154). The LOCK-IN firn analysis is ongoing, so additional firn data underlying sensitivity tests in Extended Data Fig. 6 will be published elsewhere and made available freely and immediately upon request.

Code availability

The computer codes used to support the findings of this study are available from the authors upon reasonable request.

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Acknowledgements

This work was supported by the David and Lucile Packard Foundation Science & Engineering Fellowship and by the European Commission’s Seventh Framework Programme ERC2011 under grant agreement number 291062 (ERC ICE&LASERS). We thank M. Twickler, G. Hargreaves and R. Nunn at the National Science Foundation Ice Core Facility for curating and providing ice-core samples for this study. We also thank X. Faïn, A. Lemoine and G. Teste for CO2 and CH4 laboratory measurements at IGE; G. P. Lee and W. Sturges and his team at the University of East Anglia for halocarbon measurements on the LOCK-IN canisters; J. Freitag and C. Florian Schaller at AWI and K. Fourteau at IGE for providing a high-resolution LOCK-IN density profile; E. Le Meur for evaluating the site elevation; K. Fourteau, C. F. Schaller and J. Savarino for discussions; and A. Landais for comments on the manuscript. The LOCK-IN field and scientific programme was funded by Institut Polaire Français Paul-Emile Victor programme number 1153 and Centre National de la Recherche Scientifique INSU/LEFE programme NEVE-CLIMAT. We thank the field personnel at the LOCK-IN site: D. Colin, P. Dordhain, P. Possenti, as well as P. Godon for setting up the field logistics.

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Author notes

  1. Huanting Hu

    Present address: School of Oceanology, Shanghai Jiao Tong University, Shanghai, China

Authors and Affiliations

  1. Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, USA

    Laurence Y. Yeung, Huanting Hu & Asmita Banerjee

  2. Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY, USA

    Lee. T. Murray

  3. Université Grenoble Alpes, CNRS, Institut des Géosciences de l’Environnement, Grenoble, France

    Patricia Martinerie & Jérôme Chappellaz

  4. Université Grenoble Alpes, CNRS, Grenoble Image Parole Signal Automatique (GIPSA-Lab), Grenoble, France

    Emmanuel Witrant

  5. Laboratoire des Sciences du Climat et de l’Environnement, Gif-sur-Yvette, France

    Anaïs Orsi

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Contributions

L.Y.Y., L.T.M. and J.C. designed the project. J.C., P.M. and A.O. collected the firn-air samples and analysed them for trace gases and bulk isotope ratios. L.Y.Y., H.H. and A.B. performed firn and ice-core clumped-isotope measurements. L.T.M. performed additional development of the GISS-E2.1 model and conducted the global model simulations, which were then analysed by L.Y.Y. P.M. and E.W. developed the atmospheric history inversions. L.Y.Y. wrote the paper with input from all authors.

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Correspondence to Laurence Y. Yeung.

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Extended data figures and tables

Extended Data Fig. 1 Age distribution estimates in firn and ice.

In green: gas age distributions in LOCK-IN firn at depths of 84.2 m (short-dashed line), 98.6 m (long-dashed line), 104.8 m depth (solid line) and 107.65 m (short–long dashed line). In red: gas age distribution of GISP2 ice samples. In blue: gas age distributions of WAIS Divide ice samples, estimated using a diffusivity based on NEEM-EU data (solid line) and NEEM-US data (dashed line). In purple: gas age distribution of Siple Dome ice samples.

Source Data

Extended Data Fig. 2 Comparison of the most precise model (in isotopic δ notation23) and the simplified model used to include ice data45.

Left, black stars show ∆36 (‘D-36’, in parts per thousand) data in LOCK-IN firn plotted against mean gas ages with uncertainties (±2 s.e.m., calculated using the pooled standard deviation) shown as vertical bars. Lines represent reconstructed atmospheric trends. The preferred scenario is obtained using a SCRIPPS-based O2 trend (see Methods, ‘Δ36 for firn modelling’) and is constrained by LOCK-IN firn data excluding the deepest value. The black and green solid lines show the best-guess trend obtained with the most precise and simplified models, respectively. Long-dashed lines show the uncertainty envelope. Short-dashed lines show the results of the two models when including the probably contaminated deepest data point (most precise model in green, simplified model in black). Right, δ18O18Ocor data (‘d18O18Ocor’, in parts per thousand; see Methods, ‘Δ36 for firn modelling’) in LOCK-IN firn against depth (symbols), compared with model results (lines). The four model simulations only differ in the very deep firn. The deepest data point at 107.65 m, which is probably contaminated (see text), is not shown in the left panel. Its mean gas age is 110 yr, corresponding to 1906 ad.

Source Data

Extended Data Fig. 3 Effect of gravitational fractionation on isotopic ratios in the LOCK-IN firn.

Shown are δ15N values of N2 measured at LSCE (black stars); δ18O values of O2, divided by 2, measured at LSCE (green crosses) and at Rice University (red circles); and δ18O18O values of O2, divided by 4, measured at Rice University (blue circles). The black line shows the barometric slope.

Source Data

Extended Data Fig. 4 Test of gravitational fractionation corrections for δ18O18O in LOCK-IN firn.

Shown are the corrections obtained using the δ15N value of N2 measured at LSCE (black stars), the δ18O value of O2 measured at LSCE (green crosses), the δ18O value of O2 measured at Rice University (red circles) and the ∆36 value of O2 measured at Rice University (empty blue circles).

Source Data

Extended Data Fig. 5 Comparison of firn model scenarios.

Shown are results obtained with or without the deepest LOCK-IN data point, and with constant or SCRIPPS-based O2 trend estimates, as well as forward firn model tests of atmospheric model scenarios. Top left, ∆36 data in firn and ice (LOCK-IN in green, GISP2 in red, WAIS Divide in blue, Siple Dome in purple) plotted against mean 18O18O age, compared with atmospheric trends obtained by inverse firn/ice modelling. Shown also are the ±2σ-equivalent uncertainty envelope for the inverse model (long-dashed black lines) and the best-guess trends obtained using: the SCRIPPS-based O2 scenario and excluding the deepest LOCK-IN data point (short-dashed black line); a constant-O2 scenario and excluding the deepest LOCK-IN data point (red line); the SCRIPPS-based O2 scenario and all LOCK-IN data points (dashed grey line); and a constant O2 scenario and all LOCK-IN data points (blue line). Top right, δ18O18Ocor in firn and ice (defined in Methods, ‘Δ36 for firn modelling’; LOCK-IN in green, GISP2 in red, WAIS Divide in blue, Siple Dome in purple) plotted against depth, compared with model results in firn and ice using the SCRIPPS-based O2 scenario. The solid lines show the simulation excluding the deepest LOCK-IN data point and the dashed lines correspond to the simulation with all data points. Bottom left, ∆36 data in firn and ice (same colours as in upper panels) compared with simulated profiles using the forward firn model (LOCK-IN, solid lines; WAIS Divide, long dashed lines; GISP2, short dashed lines; Siple Dome, short–long-dashed lines). Outputs shown correspond to SCRIPPS-based atmospheric concentration trends for O2 and constant values for δ18O and ∆36 (black lines); constant values for O2, δ18O and ∆36 (grey lines; results are nearly the same as the black lines); SCRIPPS-based atmospheric concentration trends for O2 and constant values for δ18O, with the +25% box model scenario for ∆36 (orange lines); the +200% box model scenario for ∆36 (blue lines); and the +300% box model scenario for ∆36 (red lines).

Source Data

Extended Data Fig. 6 Results of sensitivity tests on atmospheric trend reconstructions from the inverse firn model.

∆36 data in firn and ice (stars with ±2 s.e.m. uncertainties shown as vertical bars; LOCK-IN in green, GISP2 in red, WAIS Divide in blue, Siple Dome in purple) plotted against mean 18O18O age and compared with modelled atmospheric trends (lines). The solid black line is the preferred scenario, obtained using a SCRIPPS-based O2 concentration trend and excluding the deepest LOCK-IN data point, with its uncertainty envelope shown alongside (dashed black lines). The left panel shows the simulation that includes the deepest LOCK-IN data point (red line), a simulation with the deepest LOCK-IN data point corrected (grey star) from a maximum estimate of 10% surface air contamination (purple line), tests of the sensitivity to the optimal solution (grey lines; see Methods, ‘Sensitivity tests on atmospheric trend reconstructions’), the simulation excluding WAIS Divide data (blue line), and simulations excluding the Siple Dome data (green solid line) or excluding the Siple Dome data and using NEEM-US-data-based diffusivity to simulate WAIS Divide firn (green dashed line). The dashed grey line shows that a straight trend with a weak slope can remain in the uncertainty envelope. The right panel shows tests of LOCK-IN firn physics parameters (green; see Methods, ‘Sensitivity tests on atmospheric trend reconstructions’) and tests of LOCK-IN diffusivity constrained with field data only (blue), all nearly superimposed to the preferred trend.

Source Data

Extended Data Fig. 7 Comparison of Δ36 values in measurements of ice-core, firn and modern air.

a, Firn and ice-core Δ36 values (means of replicates) plotted against mean gas age. b, Kernel-smoothed probability density distributions of bootstrap-resampled mean values of each dataset, showing a significant (P < 0.002) difference between the means of the ice-core and firn (above 105 m) datasets. Uncertainties are omitted for clarity. Pooled standard deviations for each sample type are 0.03‰–0.04‰ (see Methods). C.E., common era.

Source Data

Extended Data Fig. 8 Modelled fraction of stratospheric air derived from GISS-E2.1 7Be simulations.

Shown are 12-month grid-scale moving averages at the surface near each of the four polar sampling sites between 1850 ad and 2015 ad.

Source Data

Extended Data Fig. 9 Instantaneous tropospheric O3 radiative forcing at the tropopause relative to 1850 ad.

Results calculated with the GISS-E2.1 (2000s) and GEOS-Chem/MERRA2 (2005) models. Maps created using the NCAR command language67.

Source Data

Extended Data Fig. 10 Solar modulation potential.

Results estimated using the sunspot number (orange line) and reconstructed from ground‐based neutron monitors and ionization chambers (from Usoskin et al.62; blue line).

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Yeung, L.Y., Murray, L.T., Martinerie, P. et al. Isotopic constraint on the twentieth-century increase in tropospheric ozone. Nature 570, 224–227 (2019). https://doi.org/10.1038/s41586-019-1277-1

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