Observations show robust near-surface trends in Southern Hemisphere tropospheric circulation towards the end of the twentieth century, including a poleward shift in the mid-latitude jet1,2, a positive trend in the Southern Annular Mode1,3,4,5,6 and an expansion of the Hadley cell7,8. It has been established that these trends were driven by ozone depletion in the Antarctic stratosphere due to emissions of ozone-depleting substances9,10,11. Here we show that these widely reported circulation trends paused, or slightly reversed, around the year 2000. Using a pattern-based detection and attribution analysis of atmospheric zonal wind, we show that the pause in circulation trends is forced by human activities, and has not occurred owing only to internal or natural variability of the climate system. Furthermore, we demonstrate that stratospheric ozone recovery, resulting from the Montreal Protocol, is the key driver of the pause. Because pre-2000 circulation trends have affected precipitation12,13,14, and potentially ocean circulation and salinity15,16,17, we anticipate that a pause in these trends will have wider impacts on the Earth system. Signatures of the effects of the Montreal Protocol and the associated stratospheric ozone recovery might therefore manifest, or have already manifested, in other aspects of the Earth system.
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Observations of total column ozone from the SBUV v8.4 satellite dataset (5° gridded, monthly and zonal mean) are available at: https://acd-ext.gsfc.nasa.gov/Data_services/merged/data/sbuv_v86_mod.int_lyr.70-17.za.r6_ext.txt.The reanalysis datasets can be downloaded from their respective webservers, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for ERA-I (https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/), the JMA Data Dissemination System (JDDS) for JRA-55 (https://jra.kishou.go.jp/JRA-55/index_en.html#download) and the Goddard Earth Sciences Data and Information Services Center (GES DIC) for MERRA2 (https://disc.gsfc.nasa.gov/datasets?keywords=%22MERRA-2%22&page=1&source=Models%2FAnalyses%20MERRA-2). Monthly mean, proxy zonal mean pressures at 40° S and 65° S, used here to compute the observed SAM index, are available at: http://www.nerc-bas.ac.uk/icd/gjma/sam.html. Model output for CanESM2 can be accessed at: http://climate-modelling.canada.ca/climatemodeldata/cgcm4/CanESM2/index.shtml. Model output from CCMI and CCMVal-2 can be accessed through the British Atmospheric Data Center (BADC) archive at: ftp://ftp.ceda.ac.uk. The two WACCM control simulations are available from the High Performance Storage System at the National Center for Atmospheric Research in Boulder, Colorado and available upon request from the corresponding author.
Code is available from the corresponding author upon reasonable request.
Swart, N. C. & Fyfe, J. C. Observed and simulated changes in the Southern Hemisphere surface westerly wind-stress. Geophys. Res. Lett. 39, L16711 (2012).
Swart, N. C., Fyfe, J. C., Gillett, N. & Marshall, G. J. Comparing trends in the Southern Annular Mode and surface westerly jet. J. Clim. 28, 8840–8859 (2015).
Thompson, D. W. J. & Solomon, S. Interpretation of recent Southern Hemisphere climate change. Science 296, 895–899 (2002).
Marshall, G. J. Trends in the Southern Annular Mode from observations and reanalyses. J. Clim. 16, 4134–4143 (2003).
Gillett, N. P. & Fyfe, J. C. Annular mode changes in the CMIP5 simulations. Geophys. Res. Lett. 40, 1189–1193 (2013).
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).
Davis, S. M. & Rosenlof, K. H. A multidiagnostic intercomparison of tropical-width time series using reanalyses and satellite observations. J. Clim. 25, 1061–1078 (2012).
Garfinkel, C. I., Waugh, D. W. & Polvani, L. M. Recent Hadley cell expansion: the role of internal atmospheric variability in reconciling modeled and observed trends. Geophys. Res. Lett. 42, 10824–10831 (2015).
Son, S.-W. et al. Impact of stratospheric ozone on Southern Hemisphere circulation change: a multimodel assessment. J. Geophys. Res. D 115, D00M07 (2010).
Polvani, L. M., Waugh, D. W., Correa, G. J. P. & Son, S.-W. Stratospheric ozone depletion: the main driver of twentieth-century atmospheric circulation changes in the Southern Hemisphere. J. Clim. 24, 795–812 (2011).
McLandress, C. et al. Separating the dynamical effects of climate change and ozone depletion. Part II: Southern Hemisphere troposphere. J. Clim. 24, 1850–1868 (2011).
Kang, S. M., Polvani, L. M., Fyfe, J. C. & Sigmond, M. Impact of polar ozone depletion on subtropical precipitation. Science 332, 951–954 (2011).
Scheff, J. & Frierson, D. M. W. Robust future precipitation declines in CMIP5 largely reflect the poleward expansion of model subtropical dry zones. Geophys. Res. Lett. 39, L18704 (2012).
Schmidt, D. F. & Grise, K. M. The response of local precipitation and sea level pressure to Hadley cell expansion. Geophys. Res. Lett. 44, 10,573–10,582 (2017).
Waugh, D. W., Primeau, F., Devries, T. & Holzer, M. Recent changes in the ventilation of the southern oceans. Science 339, 568–570 (2013).
Solomon, A., Polvani, L. M., Smith, K. L. & Abernathey, R. P. The impact of ozone depleting substances on the circulation, temperature, and salinity of the Southern Ocean: an attribution study with CESM1(WACCM). Geophys. Res. Lett. 42, 5547–5555 (2015).
Karpechko, A. Y. & Maycock, A. C. in Scientific Assessment of Ozone Depletion: 2018 Report No. 58, Ch. 5 (World Meteorological Organization, 2018).
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).
Solomon, S., Garcia, R. R., Rowland, F. S. & Wuebbles, D. J. On the depletion of Antarctic ozone. Nature 321, 755–758 (1986).
Engel, A. & Rigby, M. in Scientific Assessment of Ozone Depletion: 2018 Report No. 58, Ch. 1 (World Meteorological Organization, 2018).
Braesicke, P. & Neu, J. et al. in Scientific Assessment of Ozone Depletion: 2018 Report No. 58, Ch. 3 (World Meteorological Organization, 2018).
Solomon, S. et al. Emergence of healing in the Antarctic ozone layer. Science 353, 269–274 (2016).
Langematz, U. & Tully, M. in Scientific Assessment of Ozone Depletion: 2018 Report No. 58, Ch. 4 (World Meteorological Organization, 2018).
Engel, A. et al. A refined method for calculating equivalent effective stratospheric chlorine. Atmos. Chem. Phys. 18, 601–619 (2018).
Gillett, N. P. & Thompson, D. W. J. Simulation of recent Southern Hemisphere climate change. Science 302, 273–275 (2003).
Waugh, D. W., Garfinkel, C. I. & Polvani, L. M. Drivers of the recent tropical expansion in the Southern Hemisphere: changing SSTs or ozone depletion? J. Clim. 28, 6581–6586 (2015).
Perlwitz, J., Pawson, S., Fogt, R. L., Nielsen, J. E. & Neff, W. D. Impact of stratospheric ozone hole recovery on Antarctic climate. Geophys. Res. Lett. 35, L08714 (2008).
Son, S.-W. et al. The impact of stratospheric ozone recovery on the Southern Hemisphere westerly jet. Science 320, 1486–1489 (2008).
Polvani, L. M., Previdi, M. & Deser, C. Large cancellation, due to ozone recovery, of future Southern Hemisphere atmospheric circulation trends. Geophys. Res. Lett. 38, L04707 (2011).
Thompson, D. W. J. et al. Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci. 4, 741–749 (2011).
Arblaster, J. M. & Meehl, G. A. Contributions of external forcings to Southern Annular Mode trends. J. Clim. 19, 2896–2905 (2006).
Barnes, E. A. & Polvani, L. Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J. Clim. 26, 7117–7135 (2013).
Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
Kobayashi, S. et al. The JRA-55 Reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn 93, 5–48 (2015).
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Monahan, A. H. & Fyfe, J. C. On the nature of zonal jet EOFs. J. Clim. 19, 6409–6424 (2006).
Monahan, A. H. & Fyfe, J. C. On annular modes and zonal jets. J. Clim. 21, 1963–1978 (2008).
Solomon, A. & Polvani, L. M. Highly significant responses to anthropogenic forcings of the midlatitude jet in the Southern Hemisphere. J. Clim. 29, 3463–3470 (2016).
Sun, L., Chen, G. & Robinson, W. A. The role of stratospheric polar vortex breakdown in Southern Hemisphere climate trends. J. Atmos. Sci. 71, 2335–2353 (2014).
Solomon, S. et al. Mirrored changes in Antarctic ozone and stratospheric temperature in the late 20th versus early 21st centuries. J. Geophys. Res. D 122, 8940–8950 (2017).
Morgenstern, O. et al. Review of the formulation of present-generation stratospheric chemistry-climate models and associated external forcings. J. Geophys. Res. 115, D00M02 (2010).
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).
Cionni, I. et al. Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing. Atmos. Chem. Phys. 11, 11267–11292 (2011).
Santer, B. D. et al. Quantifying stochastic uncertainty in detection time of human-caused climate signals. Proc. Natl Acad. Sci. USA 116, 19821–19827 (2019); correction 117, 2723 (2020).
Barnes, E. A., Barnes, N. W. & Polvani, L. M. Delayed Southern Hemisphere climate change induced by stratospheric ozone recovery, as projected by the CMIP5 models. J. Clim. 27, 852–867 (2014).
Newman, P. A., Daniel, J. S., Waugh, D. W. & Nash, E. R. A new formulation of equivalent effective stratospheric chlorine (EESC). Atmos. Chem. Phys. 7, 4537-4552 (2007).
Davis, N. A. & Davis, S. M. Reconciling Hadley cell expansion trend estimates in reanalyses. Geophys. Res. Lett. 45, 11439–11446 (2018).
Grise, K. M. et al. Recent tropical expansion: natural variability or forced response? J. Clim. 32, 1551–1571 (2019).
Arora, V. K. et al. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett. 38, L05805 (2011).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5 (2011).
Sigmond, M., Fyfe, J. C. & Scinocca, J. F. Does the ocean impact the atmospheric response to stratospheric ozone depletion? Geophys. Res. Lett. 37, L12706 (2010).
Son, S.-W. et al. Tropospheric jet response to Antarctic ozone depletion: an update with Chemistry-Climate Model Initiative (CCMI) models. Environ. Res. Lett. 13, 054024 (2018).
Seviour, W. J. M., Waugh, D. W., Polvani, L. M., Correa, G. J. P. & Garfinkel, C. I. Robustness of the simulated tropospheric response to ozone depletion. J. Clim. 30, 2577–2585 (2017).
Gerber, E. P. & Son, S.-W. Quantifying the summertime response of the austral jet stream and Hadley cell to stratospheric ozone and greenhouse gases. J. Clim. 27, 5538–5559 (2014).
Smith, K. L., Neely, R. R., Marsh, D. R. & Polvani, L. M. The Specified Chemistry Whole Atmosphere Community Climate Model (SC-WACCM). J. Adv. Model. Earth Syst. 6, 883–901 (2014).
Gong, D. & Wang, S. Definition of Antarctic Oscillation index. Geophys. Res. Lett. 26, 459–462 (1999).
Solomon, A., Polvani, L. M., Waugh, D. W. & Davis, S. M. Contrasting upper and lower atmospheric metrics of tropical expansion in the Southern Hemisphere. Geophys. Res. Lett. 43, 10496–10503 (2016).
Davis, N. & Birner, T. On the discrepancies in tropical belt expansion between reanalyses and climate models and among tropical belt width metrics. J. Clim. 30, 1211–1231 (2017).
Waugh, D. W. et al. Revisiting the relationship among metrics of tropical expansion. J. Clim. 31, 7565–7581 (2018).
Weatherhead, E. C. et al. Factors affecting the detection of trends: statistical considerations and applications to environmental data. J. Geophys. Res. 103, 17149–17161 (1998).
Swart, N. C., Gille, S. T., Fyfe, J. C. & Gillett, N. P. Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nat. Geosci. 11, 836–841 (2018).
We thank J. Daniel, N. A. Davis, S. M. Davis and B. Santer for conversations on this work. This work was funded by grants from the US National Science Foundation (NSF) to Columbia University and a fellowship from the Cooperative Institute for Research in Environmental Sciences (CIRES).
The authors declare no competing interests.
Peer review information Nature thanks Sandip Dhomse, Alexey Karpechko, Wenshou Tian and Guang Zeng for their contribution to the peer review of this work.
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Extended data figures and tables
The timeseries is for the DJF season. Thin black lines and the grey shaded envelope represent the average across four reanalysis products (ERA-I, JRA-55, MERRA2-ana and MERRA2-asm) and their minimum to maximum range. The thin line represents the unsmoothed quantity and the thick line represents centred 3-yr smoothed values. Two piecewise continuous linear trend lines for the unsmoothed data (dashed lines) are drawn for the periods 1980–2000 and 2000–2017 (the values for their slopes are provided in Extended Data Table 1).
a, EESC (note the inverted left y axis) for polar winter conditions and Antarctic TCO for the SON season as measured by SBUV (in Dobson units, DU). b–d, Circulation metrics for the JJA season. b, Position of the SH mid-latitude jet (in degrees latitude) in reanalysis data. c, SAM index (note the inverted y axis) as derived from reanalysis data and from station observations4. d, Latitude of the edge of the SH Hadley cell in reanalysis data. Thin black lines and grey shaded envelopes in b–d represent the average across four reanalysis products (ERA-I, JRA-55, MERRA2-ana and MERRA2-asm) and their minimum to maximum range. Thin lines represent unsmoothed quantities and thick lines represent centred 3-yr smoothed values. Two piecewise continuous linear trend lines for the unsmoothed data (dashed lines) are drawn for the periods 1980–2000 and 2000–2017.
a, b, Latitude–altitude cross-sections of zonal average temperature trends (colour shading) for SON are shown for the depletion period (a) and recovery period (b). Trends are for the four-reanalysis average. Contours show climatological values (in °C). Hatching indicates areas where trends are not significant at the 95% confidence level according to a two-tailed Student’s t-test using the standard error in the slopes.
The monthly evolution of trends in latitudinally averaged (50–70° S) zonal wind (colour shading) for DJF are shown for the depletion period (a, d, g), recovery period (b, e, h) and the change between them (c, f, i). a–i, Trends for the four-reanalysis average (a–c) and the ALL fingerprints of CanESM2 (d–f) and the CCMs (g–i) are shown. Contours show climatological values (in m s−1; in c, f and i, the climatology is over the entire change period). The hatching in d–i shows areas where the reanalysis trends lie outside the 5th–95th percentile range of the simulated ALL ensemble trends.
a–e, Standard box and whisker plots showing DJF trends in jet position (a, d; degrees latitude per decade), the SAM index (b; per decade) and the Hadley cell edge (c, e; degrees latitude per decade) across the CanESM2 (a–c) and CCM (d, e) ensembles. Numbers designate the number of ensemble members showing positive (red) and negative (blue) trends. The cross symbols represent the average trends across the four reanalysis products. For the SAM index, the triangles represent trends in station-based observations.
Extended Data Fig. 6 Simulated zonal average zonal wind trends due to anthropogenic aerosols and natural forcing.
Latitude–altitude cross-sections of zonal average zonal wind trends (colour shading) for DJF are shown for the depletion period (a, d), recovery period (b, e) and the change between them (c, f). a–f, Fingerprints for the single forcings: AA (a–c) and NAT (d–f) as simulated by CanESM2. For illustrative purposes, the contours represent the ALL forcing climatologies (in m s−1; in c and f, the climatology is over the entire change period).
The main analysis (Fig. 4a) considers a one-signal analysis against the ALL fingerprint, and a two-signal analysis against the OZ and GHG fingerprints, where confidence intervals are derived from the ensemble spread, and over the domain shown in Figs. 2, 3 (10–850 hPa, 0–90° S). The sensitivity tests shown here are variations on the main analysis that consider a four-signal analysis of the OZ, GHG, AA and NAT fingerprints (black), the four-signal analysis with confidence intervals derived from a CanESM2 piControl run (Methods; red) and a limited domain of analysis (100–850 hPa, 30–90° S) (blue). The vertical bars represent the 95% uncertainty (2.5th–97.5th percentiles) and the horizontal bars represent the 90% uncertainty (5th–90th percentiles).
Each case shows a one-signal analysis against the ALL fingerprint, and a two-signal analysis against the OZ and GHG fingerprints. The main analysis (Fig. 4b) performs the analysis across 50 model simulations, with confidence intervals derived from a WACCM piControl run, and over the domain shown in Figs. 2, 3 (10–850 hPa, 0–90° S). The sensitivity tests shown here are variations on the main analysis that consider a subset of models that performed the fODS and fGHG sensitivity simulations (total 30 members) (black); confidence intervals derived from a WACCM piControl run containing the 11-yr solar cycle (Methods; red) and a limited domain of analysis (100–850 hPa, 30–90° S) (blue). The vertical bars represent the 95% uncertainty (2.5th–97.5th percentiles) and the horizontal bars represent the 90% uncertainty (5th–90th percentiles).
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Banerjee, A., Fyfe, J.C., Polvani, L.M. et al. A pause in Southern Hemisphere circulation trends due to the Montreal Protocol. Nature 579, 544–548 (2020). https://doi.org/10.1038/s41586-020-2120-4
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