A pause in Southern Hemisphere circulation trends due to the Montreal Protocol

Abstract

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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Fig. 1: Timeseries of ozone and near-surface circulation metrics.
Fig. 2: Zonal average zonal wind trends in reanalysis data and models.
Fig. 3: Simulated zonal average zonal wind trends due to stratospheric ozone and GHGs.
Fig. 4: Scaling factors from the detection and attribution analysis.

Data availability

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 availability

Code is available from the corresponding author upon reasonable request.

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Acknowledgements

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).

Author information

A.B. proposed the paper, performed the analysis and wrote the paper. A.B. and J.C.F. designed the paper and interpreted the results, with contributions from L.M.P. and D.W. K-L.C. advised on statistical methods.

Correspondence to Antara Banerjee.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks Sandip Dhomse, Alexey Karpechko, Wenshou Tian and Guang Zeng 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 figures and tables

Extended Data Fig. 1 Timeseries of mid-latitude jet strength.

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).

Extended Data Fig. 2 Timeseries of ozone and near-surface circulation metrics.

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). bd, 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 bd 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.

Extended Data Fig. 3 Zonal average temperature trends.

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.

Extended Data Fig. 4 Monthly trends in mid-latitude zonal wind.

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 (beh) and the change between them (c, f, i). ai, Trends for the four-reanalysis average (ac) and the ALL fingerprints of CanESM2 (df) and the CCMs (gi) 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 di shows areas where the reanalysis trends lie outside the 5th–95th percentile range of the simulated ALL ensemble trends.

Extended Data Fig. 5 Simulated trends in near-surface circulation metrics.

ae, 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 (ac) 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 (cf). af, Fingerprints for the single forcings: AA (ac) and NAT (df) 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).

Extended Data Fig. 7 Scaling factors from detection and attribution sensitivity tests for CanESM2.

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).

Extended Data Fig. 8 Scaling factors from detection and attribution sensitivity tests for the CCMs.

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).

Extended Data Table 1 Trends in ozone and circulation metrics and change-point testing
Extended Data Table 2 List of CCMs

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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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