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Fingerprinting the recovery of Antarctic ozone

Nature volume 639, pages 646–651 (2025)Cite this article

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Abstract

The Antarctic ozone ‘hole’ was discovered in 1985 (ref. 1) and man-made ozone-depleting substances (ODSs) are its primary cause2. Following reductions of ODSs under the Montreal Protocol3, signs of ozone recovery have been reported, based largely on observations and broad yet compelling model–data comparisons4. Although such approaches are highly valuable, they do not provide rigorous statistical detection of the temporal and spatial structure of Antarctic ozone recovery in the presence of internal climate variability. Here we apply pattern-based detection and attribution methods as used in climate-change studies5,6,7,8,9,10,11 to separate anthropogenically forced ozone responses from internal variability, relying on trend pattern information as a function of month and height. The analysis uses satellite observations together with single-model and multi-model ensemble simulations to identify and quantify the month–height Antarctic ozone recovery ‘fingerprint’12. We demonstrate that the data and simulations show compelling agreement in the fingerprint pattern of the ozone response to decreasing ODSs since 2005. We also show that ODS forcing has enhanced ozone internal variability during the austral spring, influencing detection of forced responses and their time of emergence. Our results provide robust statistical and physical evidence that actions taken under the Montreal Protocol to reduce ODSs are indeed resulting in the beginning of Antarctic ozone recovery, defined as increases in ozone consistent with expected month–height patterns.

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Fig. 1: Month–height patterns of ozone trends in observations and simulations.
Fig. 2: Ozone variability modulated by external forcing.
Fig. 3: The local S/N pattern of ozone changes.
Fig. 4: S/N characteristics for the overall month–height fingerprint pattern.

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Data availability

MLS and OMI satellite data are publicly available at https://disc.gsfc.nasa.gov. CCMI model outputs are available at https://archive.ceda.ac.uk and the CESM model outputs are available at https://www.earthsystemgrid.org. All of the pre-processed model data (for example, monthly mean ozone averaged over 66° S–82° S from the CCMI and the WACCM and interpolated onto the MLS vertical coordinates) are available at Zenodo (https://doi.org/10.5281/zenodo.14497873)57.

Code availability

The code used to generate all of the figures in this analysis is available at Zenodo (https://doi.org/10.5281/zenodo.14497873)57.

References

  1. Farman, J. C., Gardiner, B. G. & Shanklin, J. D. Large losses of total ozone in Antarctica reveal seasonal ClOx/NOx interaction. Nature 315, 207–210 (1985).

    Article  ADS  CAS  Google Scholar 

  2. Solomon, S., Garcia, R. R., Rowland, F. S. & Wuebbles, D. J. On the depletion of Antarctic ozone. Nature 321, 755–758 (1986).

    Article  ADS  CAS  MATH  Google Scholar 

  3. Laube, J. C. & Tegtmeier, S. in Scientific Assessment of Ozone Depletion: 2022 Ch. 1 51–114 (World Meteorological Organization, 2022).

  4. Chipperfield, M. P. & Santee, M. L. in Scientific Assessment of Ozone Depletion: 2022 Ch. 4 215–270 (World Meteorological Organization, 2022).

  5. Santer, B. D. et al. Exceptional stratospheric contribution to human fingerprints on atmospheric temperature. Proc. Natl Acad. Sci. 120, e2300758120 (2023).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  6. Terray, L. et al. Near-surface salinity as nature’s rain gauge to detect human influence on the tropical water cycle. J. Clim. 25, 958–977 (2012).

    Article  ADS  MATH  Google Scholar 

  7. Stott, P. A., Sutton, R. T. & Smith, D. M. Detection and attribution of Atlantic salinity changes. Geophys. Res. Lett. 35, L21702 (2008).

    Article  ADS  Google Scholar 

  8. Gillett, N. P., Fyfe, J. C. & Parker, D. E. Attribution of observed sea level pressure trends to greenhouse gas, aerosol, and ozone changes. Geophys. Res. Lett. 40, 2302–2306 (2013).

    Article  ADS  CAS  Google Scholar 

  9. Christidis, N. & Stott, P. A. Changes in the geopotential height at 500 hPa under the influence of external climatic forcings. Geophys. Res. Lett. 42, 10,798–10,806 (2015).

    Article  MATH  Google Scholar 

  10. Shi, J.-R., Santer, B. D., Kwon, Y.-O. & Wijffels, S. E. The emerging human influence on the seasonal cycle of sea surface temperature. Nat. Clim. Change 14, 364–372 (2024).

    Article  ADS  MATH  Google Scholar 

  11. Santer, B. D. et al. Robust anthropogenic signal identified in the seasonal cycle of tropospheric temperature. J. Clim. 35, 6075–6100 (2022).

    Article  ADS  MATH  Google Scholar 

  12. Hasselmann, K. Optimal fingerprints for the detection of time-dependent climate change. J. Clim. 6, 1957–1971 (1993).

    Article  ADS  MATH  Google Scholar 

  13. Santee, M. L. et al. Prolonged and pervasive perturbations in the composition of the Southern Hemisphere midlatitude lower stratosphere from the Australian New Year’s fires. Geophys. Res. Lett. 49, e2021GL096270 (2022).

    Article  ADS  MATH  Google Scholar 

  14. Bernath, P., Boone, C. & Crouse, J. Wildfire smoke destroys stratospheric ozone. Science 375, 1292–1295 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Solomon, S. et al. Chlorine activation and enhanced ozone depletion induced by wildfire aerosol. Nature 615, 259–264 (2023).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  16. Wang, X. et al. Stratospheric climate anomalies and ozone loss caused by the Hunga Tonga-Hunga Ha’apai volcanic eruption. J. Geophys. Res. Atmos. 128, e2023JD039480 (2023).

    Article  ADS  Google Scholar 

  17. Zhang, J. et al. Chemistry contribution to stratospheric ozone depletion after the unprecedented water-rich Hunga Tonga eruption. Geophys. Res. Lett. 51, e2023GL105762 (2024).

    Article  ADS  CAS  Google Scholar 

  18. Wohltmann, I., Santee, M. L., Manney, G. L. & Millán, L. F. The chemical effect of increased water vapor from the Hunga Tonga-Hunga Ha’apai eruption on the Antarctic ozone hole. Geophys. Res. Lett. 51, e2023GL106980 (2024).

    Article  ADS  Google Scholar 

  19. Manney, G. L. et al. Siege in the southern stratosphere: Hunga Tonga-Hunga Ha’apai water vapor excluded from the 2022 Antarctic polar vortex. Geophys. Res. Lett. 50, e2023GL103855 (2023).

    Article  ADS  CAS  Google Scholar 

  20. Kessenich, H. E., Seppälä, A. & Rodger, C. J. Potential drivers of the recent large Antarctic ozone holes. Nat. Commun. 14, 7259 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  21. Hassler, B. & Young, P. J. in Scientific Assessment of Ozone Depletion: 2022 Ch. 3 153–214 (World Meteorological Organization, 2022).

  22. Santer, B. D. et al. Accounting for the effects of volcanoes and ENSO in comparisons of modeled and observed temperature trends. J. Geophys. Res. Atmos. 106, 28033–28059 (2001).

    Article  ADS  MATH  Google Scholar 

  23. Dhomse, S. S. et al. Estimates of ozone return dates from Chemistry-Climate Model Initiative simulations. Atmos. Chem. Phys. 18, 8409–8438 (2018).

    Article  ADS  CAS  MATH  Google Scholar 

  24. Zeng, G. et al. Attribution of stratospheric and tropospheric ozone changes between 1850 and 2014 in CMIP6 models. J. Geophys. Res. Atmos. 127, e2022JD036452 (2022).

    Article  ADS  CAS  Google Scholar 

  25. Robertson, F. et al. Signal-to-noise calculations of emergence and de-emergence of stratospheric ozone depletion. Geophys. Res. Lett. 50, e2023GL104246 (2023).

    Article  ADS  Google Scholar 

  26. Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).

    Article  ADS  MATH  Google Scholar 

  27. Waters, J. W. et al. The Earth Observing System Microwave Limb Sounder (EOS MLS) on the Aura satellite. IEEE Trans. Geosci. Remote Sens. 44, 1075–1092 (2006).

    Article  ADS  MATH  Google Scholar 

  28. Morgenstern, O. et al. Review of the global models used within phase 1 of the Chemistry–Climate Model Initiative (CCMI). Geosci. Model Dev. 10, 639–671 (2017).

    Article  ADS  MATH  Google Scholar 

  29. Marsh, D. R. et al. Climate change from 1850 to 2005 simulated in CESM1(WACCM). J. Clim. 26, 7372–7391 (2013).

    Article  ADS  MATH  Google Scholar 

  30. Garcia, R. R., Smith, A. K., Kinnison, D. E., de la Cámara, Á. & Murphy, D. J. Modification of the gravity wave parameterization in the Whole Atmosphere Community Climate Model: motivation and results. J. Atmos. Sci. 74, 275–291 (2017).

    Article  ADS  Google Scholar 

  31. Wargan, K., Weir, B., Manney, G. L., Cohn, S. E. & Livesey, N. J. The anomalous 2019 Antarctic ozone hole in the GEOS Constituent Data Assimilation System with MLS observations. J. Geophys. Res. Atmos. 125, e2020JD033335 (2020).

    Article  ADS  CAS  Google Scholar 

  32. Solomon, S. et al. Emergence of healing in the Antarctic ozone layer. Science 353, 269–274 (2016).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  33. Manabe, S. & Wetherald, R. T. Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci. 24, 241–259 (1967).

    Article  ADS  CAS  MATH  Google Scholar 

  34. Haigh, J. D. & Pyle, J. A. Ozone perturbation experiments in a two-dimensional circulation model. Q. J. R. Meteorol. Soc. 108, 551–574 (1982).

    ADS  CAS  MATH  Google Scholar 

  35. Molina, M. J. & Rowland, F. S. Stratospheric sink for chlorofluoromethanes: chlorine atom-catalysed destruction of ozone. Nature 249, 810–812 (1974).

    Article  ADS  CAS  Google Scholar 

  36. Solomon, S., Portmann, R. W., Sasaki, T. & Hofman, D. J. Four decades of ozonesonde measurements over Antarctica. J. Geophys. Res. Atmos. 110, D21311 (2005).

    Article  ADS  Google Scholar 

  37. Levelt, P. F. et al. The ozone monitoring instrument. IEEE Trans. Geosci. Remote Sens. 44, 1093–1101 (2006).

    Article  ADS  MATH  Google Scholar 

  38. Schoeberl, M. R. & Hartmann, D. L. The dynamics of the stratospheric polar vortex and its relation to springtime ozone depletions. Science 251, 46–52 (1991).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  39. Thompson, D. W. J. et al. Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci. 4, 741–749 (2011).

    Article  ADS  CAS  MATH  Google Scholar 

  40. Zhou, X. et al. Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha’apai water vapor. Geophys. Res. Lett. 51, e2023GL107630 (2024).

    Article  ADS  Google Scholar 

  41. Eric Klobas, J., Wilmouth, D. M., Weisenstein, D. K., Anderson, J. G. & Salawitch, R. J. Ozone depletion following future volcanic eruptions. Geophys. Res. Lett. 44, 7490–7499 (2017).

    Article  ADS  CAS  Google Scholar 

  42. Chim, M. M. et al. Climate projections very likely underestimate future volcanic forcing and its climatic effects. Geophys. Res. Lett. 50, e2023GL103743 (2023).

    Article  ADS  CAS  MATH  Google Scholar 

  43. Revell, L. E., Bodeker, G. E., Huck, P. E., Williamson, B. E. & Rozanov, E. The sensitivity of stratospheric ozone changes through the 21st century to N2O and CH4. Atmos. Chem. Phys. 12, 11309–11317 (2012).

    Article  ADS  CAS  Google Scholar 

  44. Stone, K. A., Solomon, S. & Kinnison, D. E. On the identification of ozone recovery. Geophys. Res. Lett. 45, 5158–5165 (2018).

    Article  ADS  CAS  MATH  Google Scholar 

  45. Chipperfield, M. P. & Bekki, S. Opinion: Stratospheric ozone – depletion, recovery and new challenges. Atmos. Chem. Phys. 24, 2783–2802 (2024).

    Article  ADS  MATH  Google Scholar 

  46. Hubert, D. et al. Ground-based assessment of the bias and long-term stability of 14 limb and occultation ozone profile data records. Atmos. Meas. Tech. 9, 2497–2534 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  47. Froidevaux, L. et al. Validation of Aura Microwave Limb Sounder stratospheric ozone measurements. J. Geophys. Res. Atmos. 113, D15S20 (2008).

    ADS  MATH  Google Scholar 

  48. World Meteorological Organization (WMO). Scientific Assessment of Ozone Depletion: 2010 (World Meteorological Organization, 2011).

  49. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213 (2011).

    Article  ADS  CAS  MATH  Google Scholar 

  50. Stone, K. A., Solomon, S., Thompson, D. W. J., Kinnison, D. E. & Fyfe, J. C. On the Southern Hemisphere stratospheric response to ENSO and its impacts on tropospheric circulation. J. Clim. 35, 1963–1981 (2022).

    Article  ADS  Google Scholar 

  51. Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  ADS  MATH  Google Scholar 

  52. Solomon, A. et al. Distinguishing the roles of natural and anthropogenically forced decadal climate variability: implications for prediction. Bull. Am. Meteorol. Soc. 92, 141–156 (2011).

    Article  ADS  MATH  Google Scholar 

  53. Manney, G. L. et al. Solar occultation satellite data and derived meteorological products: sampling issues and comparisons with Aura Microwave Limb Sounder. J. Geophys. Res. Atmos. 112, D24S50 (2007).

    Article  ADS  Google Scholar 

  54. Millán, L. F. et al. Multi-parameter dynamical diagnostics for upper tropospheric and lower stratospheric studies. Atmos. Meas. Tech. 16, 2957–2988 (2023).

    Article  MATH  Google Scholar 

  55. Manney, G. L. et al. Jet characterization in the upper troposphere/lower stratosphere (UTLS): applications to climatology and transport studies. Atmos. Chem. Phys. 11, 6115–6137 (2011).

    Article  ADS  CAS  MATH  Google Scholar 

  56. Lawrence, Z. D., Manney, G. L. & Wargan, K. Reanalysis intercomparisons of stratospheric polar processing diagnostics. Atmos. Chem. Phys. 18, 13547–13579 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, P. et al. Data and code for “Fingerprinting the Recovery of Antarctic Ozone”. Zenodo https://doi.org/10.5281/zenodo.14497873 (2024).

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Acknowledgements

We thank C. Deser and P. Lin for helpful discussions. We also thank L. Horowitz and M. Lin for providing GFDL model data for this analysis. S.S. and P.W. gratefully acknowledge support from the Atmospheric Chemistry division of the National Science Foundation under grant nos. 2316980 and 2128617. B.D.S. was supported by the Francis E. Fowler IV Center for Ocean and Climate at Woods Hole Oceanographic Institution (WHOI). D.E.K. was financed in part by NASA grant 80NSSC19K0952. Q.F. was supported in part by NSF grant AGS-2202812. The Community Earth System Model (CESM) project is supported by the National Science Foundation and the Office of Science of the U.S. Department of Energy. We gratefully acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation. Work at the Jet Propulsion Laboratory, California Institute of Technology, was carried out under a contract with the National Aeronautics and Space Administration (NASA; 80NM0018D0004).

Author information

Authors and Affiliations

  1. Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Peidong Wang, Susan Solomon & Kane A. Stone

  2. Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Benjamin D. Santer

  3. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    Benjamin D. Santer

  4. Atmospheric Chemistry Observations and Modeling, National Science Foundation National Center for Atmospheric Research, Boulder, CO, USA

    Douglas E. Kinnison & Jun Zhang

  5. Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA

    Qiang Fu

  6. NorthWest Research Associates, Socorro, NM, USA

    Gloria L. Manney

  7. New Mexico Institute of Mining and Technology, Socorro, NM, USA

    Gloria L. Manney

  8. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Luis F. Millán

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Contributions

P.W., S.S. and B.D.S. designed the study. D.E.K. designed and performed the WACCM simulations. P.W. analysed the data and produced the figures. P.W. and S.S. drafted the initial text. B.D.S., Q.F., K.A.S., J.Z., G.L.M. and L.F.M. contributed substantially to the interpretation of findings.

Corresponding authors

Correspondence to Peidong Wang or Susan Solomon.

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Extended data figures and tables

Extended Data Fig. 1 Variability superimposed on external forcing.

Ozone trends from 2005–2018 in individual WACCM realizations (top two rows, with the realization number indicated in the title of each panel) and in individual models from the CCMI-1 (last four rows, with model names indicated in the title of each panel) under the refC2 scenario. The MLS observed trend is shown in the bottom-right panel.

Extended Data Fig. 2 Ozone trends owing to different forcings.

Ensemble-mean ozone trends (2005–2018) averaged over ten WACCM members for each scenario. Results indicate the forced responses in ozone owing to: combined time-evolving GHG and ODS forcing (refC2) (a), evolving GHG forcing only (fODS) (b) and evolving ODS forcing only (fGHG) (c). A detailed description of each scenario is given in Methods. The observed ozone trend from the MLS in 2005–2018 is also shown in panel d for visual comparison with forced ozone trends owing to different forcings.

Extended Data Fig. 3 Ozone variability modulated by external forcing.

Similar to Fig. 2 but for the CCMI models. Note that the spread in ozone trends in the CCMI arises not only from internal variability but also from cross-model differences and errors (discussed in detail in Methods). This convolving of internal variability with model differences and errors contributes to the larger noise in panel a compared with the noise derived from the WACCM single-model refC2 ensemble in Fig. 2b.

Extended Data Fig. 4 The local S/N characteristics of ozone changes.

Similar to Fig. 3 but with results for the CCMI models.

Extended Data Fig. 5 The local S/N pattern of ozone changes.

Similar to Fig. 3 but for signal and noise estimates based on ozone trends over 2005–2023 (rather than over 2005–2018). There is a marked decrease in the MLS ozone in October and November in the mid-stratosphere and in January to May in the lowermost stratosphere. This raises the question of whether these two features may be linked. The bottom panel shows the time series of ozone mixing ratios from the MLS in October at 12.1 hPa (blue) and in February at 82.5 hPa (orange). The decrease in February at 82.5 hPa is mainly because of continued low ozone after 2021 (panel c), which lags the behaviour in October by about a season, suggesting that they may be linked.

Extended Data Fig. 6 Time of emergence of springtime TCO recovery.

Similar to the top and bottom panels in Fig. 3 except the trends are the TCO from the WACCM and the OMI.

Extended Data Fig. 7 Map of the MLS ozone anomalies and polar vortex in October at 12.1 hPa.

The colour shadings indicate the ozone anomaly relative to the zonal mean in 2005–2018. Because the location of the polar vortex can vary considerably over time, dotted markers indicate that the polar vortex has occupied a given grid box for more than 25% of the time in that month. Black dashed lines encompass the area between latitudes 66° S and 82° S.

Extended Data Fig. 8 MLS springtime ozone trends (2005–2018) using fixed latitude averages versus vortex averages.

Monthly mean ozone trends in September, October and November from 2005 to 2018 are shown as blue lines (in which ozone is averaged across fixed latitudes between 66° S and 82° S) and red lines (in which ozone is averaged inside the polar vortex) at different pressure levels. A detailed description of the vortex calculation is provided in Methods. Dots and crosses indicate trends significant at the 5% and 10% levels, respectively. Note that the statistical confidence in this figure is based solely on P-values from linear regression. It does not rely on internal variability noise generated by the WACCM or the CCMI model, as shown in the other figures.

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Wang, P., Solomon, S., Santer, B.D. et al. Fingerprinting the recovery of Antarctic ozone. Nature 639, 646–651 (2025). https://doi.org/10.1038/s41586-025-08640-9

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