Geophysical Research Letters

Observed and simulated changes in the Southern Hemisphere surface westerly wind-stress


  • N. C. Swart,

    Corresponding author
    1. School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
      Corresponding author: N. C. Swart, School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada. (
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  • J. C. Fyfe

    1. Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, British Columbia, Canada
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Corresponding author: N. C. Swart, School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada. (


[1] Changes in the position and strength of the Southern Hemisphere surface westerlies have significant implications for ocean circulation and the global carbon cycle. Here we compare the climatologies, as well as the trends, in the position and strength of the surface westerly wind-stress jet in reanalyses with the Coupled Model Intercomparison Project (CMIP) phase 3 and phase 5 models over the historical period from 1979–2010. We show that both the CMIP3 and CMIP5 models exhibit an equatorward biased climatological jet position. The reanalyses and climate models both show significant trends in annual mean jet strength, though the climate models underestimate the strengthening. Neither reanalyses nor models show a robust trend in annual mean jet position over the historical period, though significant trends do occur in the Austral summer position. We also compare the response of the CMIP3 and CMIP5 model wind-stresses to a range of anthropogenic forcing scenarios for the 21st century.

1. Introduction

[2] The latitudinal position and the strength of the Southern Hemisphere (SH) surface westerly winds influences the rate of the oceanic meridional overturning circulation. They do so by controlling the Indo-Atlantic salt flux via the Agulhas Leakage [Beal et al., 2011], and by governing the rate of deep upwelling in the Southern Ocean [Marshall and Speer, 2012]. This connection between the winds and oceanic overturning modulates the global carbon cycle [Ito et al., 2010], making accurate knowledge of changes in the winds vital to understanding the fate of anthropogenic carbon [Le Quéré et al., 2007]. Similarly, in climate models, correct simulation of the winds and their time-evolution under anthropogenic forcing is key to robust projections of future climate change [Swart and Fyfe, 2012; Zickfeld et al., 2007; Russell et al., 2006a].

[3] Observations and reanalyses show a positive trend in the Southern Annular Mode (SAM), the principal mode of atmospheric variability in the Southern Hemisphere (Figure 1a) [Marshall, 2003]. It is often asserted that a poleward shift and strengthening of the SH surface westerly wind jet are synonymous with the positive trend in the SAM. However, while the strength of the jet appears to have increased robustly in the reanalyses (Figure 1c), its annual mean position has not obviously experienced a poleward shift since 1979 (Figure 1b). Prior to the start of the satellite era in 1979, the position of the jet varied significantly among the available reanalyses, with the large trends in the NCEP-NCAR Reanalysis 1 SAM index over the 1949–1978 period known to be spurious [Marshall, 2003].

Figure 1.

Historical changes (a) in the annual mean Southern Annular Mode index, (b) in the SH surface westerlies latitudinal position, and (c) strength, of the zonal-mean zonal wind-stress. Changes are shown for four reanalysis products, and in Figure 1a for updated observations fromMarshall [2003].

[4] In the Coupled Model Intercomparison Project (CMIP) phase 3 climate models, the magnitude of the change in westerly wind jet position in time has been shown to depend on the climatological jet position over the 20th century [Kidston and Gerber, 2010]. The 20th century westerlies simulated by the CMIP3 models are on average weaker and equatorward biased in position relative to observations [Fyfe and Saenko, 2006; Russell et al., 2006b]. A validation of the climatology and trends in the SH westerlies as simulated by the new CMIP5 models is thus a priority for understanding the ocean circulation and carbon cycle dynamics in the CMIP5 results.

[5] We use four reanalyses over the historical period from 1979 to 2010 to produce an observationally-based estimate of the climatology and trends in the SH surface westerly wind-stress jet. We then use this reanalysis based estimate to validate the jet climatology and trends simulated by the CMIP3 and CMIP5 climate models over the historical period, and finally we consider the response of the climate model winds to future scenarios of anthropogenic forcing.

2. Data and Methods

2.1. Observations and Reanalyses

[6] We use the mean sea-level pressure (MSLP) and the surface zonal wind-stress from the four reanalysis products: NCEP-NCAR Reanalysis 1 (R1) [Kalnay et al., 1996], NCEP-DOE Reanalysis 2 (R2) [Kanamitsu et al., 2002], ECMWF ERA-Interim Reanalysis (ERA-Interim) [Dee et al., 2011] and NOAA-CIRES Twentieth Century Reanalysis Version 2 (20CR) [Compo et al., 2011]. The NASA MERRA and NCEP CFSR reanalyses have not been included because these two products show significant disagreement with the other reanalyses in their strength trends, which we discuss in the auxiliary material. In addition we use the Southern Annular Mode index updated online from Marshall [2003], an empirical measure based on zonal means of discrete station data.

[7] For the reanalyses the Southern Annular Mode index was computed from the MSLP fields, as per Marshall [2003] as: SAM = P40°S − P65°S. Here P40°S and P65°S are the normalized monthly zonal MSLP at 40°S and 65°S, respectively. We use 1979 to 2010 as the averaging period in the normalization of the reanalyses SAM index. In the updated Marshall [2003] data we renormalize to the same period by subtracting the 1979 to 2010 mean.

2.2. Climate Model Data

[8] We use the surface zonal wind-stress fields for the 20th century simulation of 23 CMIP3 models and for the historical simulations of 21 CMIP5 models (see Table S1 inText S1). Where multiple realizations exist for an individual model, we use only the first. To enable comparison with reanalysis data up until present day, the CMIP3 20th century simulations were extended from 2001 to 2010 using the SRES A1B simulations. The CMIP5 historical simulations were extended from 2005 to 2010 using the RCP4.5 simulations. The SRES and RCP scenarios are similar over this short extension period, and therefore the choice of scenario will not affect our results.

[9] In considering simulated changes in the winds over the 1979–2100 period, the available subset of the above CMIP5 models wind-stress fields was used: 15 models for RCP2.6, 19 models for RCP4.5, 12 models for RCP6.0, 17 models for RCP8.5. The available subset of CMIP3 models over the 1979–2100 period: 18 models for SRES A2 and 23 models for SRES A1B. For the comparison of the response of the jet to CO2 forcing, the available subset for the 1% per year increase in CO2experiments to doubling, including 21 CMIP3 models and 17 CMIP5 models was used. The climate model data were made available through the World Climate Research Programme's (WCRP's) CMIP3 and CMIP5 multi-model datasets.

2.3. Definitions and Trend Calculations

[10] For all calculations, the climate model and reanalysis wind-stress data was first interpolated onto a common 0.5-by-0.5 degree horizontal grid and to a common monthly no-leap-year calendar in time. For all time-series analyses the latitudinal position of the SH westerly wind-jet was defined as a search for the latitude of the maximum in the zonal-mean zonal surface wind-stress between 70° and 20°S. The strength of the jet was defined as the stress at this position. Where indicated we present results for the ensemble mean of the reanalyses, CMIP3 and CMIP5 models. In these cases, we have determined the latitudinal position and maximum strength of the SH surface westerly wind-stress in each reanalysis product and individual model, and then computed the ensemble mean as the average over the appropriate number of reanalyses or models. Temporal trends in position and strength were computed using a linear least squares fit to the ensemble mean data, which has been monthly, seasonally or annually averaged. The confidence interval of the trends are based on the variance of the ensemble mean, and account for auto-correlation followingSanter et al. [2000].

3. Results

3.1. Climatological Position and Strength

[11] Over the historical period from 1979 to 2010 the reanalyses show agreement on the latitudinal position of the zonal wind-stress maximum, with a zonal-mean position near 52°S (Figure 2a). Both the CMIP3 and CMIP5 models have a climatological zonal mean position which is statistically significantly equatorward biased relative to the reanalyses. The CMIP5 models do however represent an improvement over the CMIP3 models, with a more accurate position, and a smaller inter-model spread. When the latitudinal position of the maximum wind-stress is considered by longitude, it can be seen that the equatorward position bias in the climate models occurs at all longitudes. The bias is predominant over the Pacific Ocean, because the climate model winds fail to make the sharp southward-turn near 150°E evident in the reanalyses (Figure 2c).

Figure 2.

Climatologies of the SH surface westerly wind-stress position and strength over 1979–2010. Four reanalyses, 23 CMIP3 models and 21 CMIP5 models are compared in notched box plots of climatological (a) position and (b) strength of the zonal-mean zonal wind-stress; (c) latitudinal position by longitude for the reanalyses, CMIP3 and CMIP5 ensemble means and (d) strength by longitude for the respective ensemble means. In Figures 2a and 2b whiskers extend to the most extreme data point within 1.5 times the interquartile range, and red plus symbols are outliers. The notches represent a robust estimate of the uncertainty about the medians for box-to-box comparison. Boxes whose notches do not overlap indicate that the medians of the two groups differ at the 5% significance level. Envelopes in Figures 2c and 2d show the 95% confidence interval. Dashed black lines indicate the ocean basin boundaries.

[12] The climatological zonal-mean strength of the wind-stress is similar between the reanalyses and climate models, near 0.19 Pa (Figure 2b). Again, the CMIP5 models show a far tighter spread with no outliers, in contrast to the CMIP3 models which had a large spread in strength with two outliers having low wind-stresses of around 0.13 Pa. Nonetheless, the climate models in general exhibit a slightly lower wind-stress than the reanalyses over the Indian and Pacific ocean basins (Figure 2d).

3.2. Historical Trends in Position and Strength

[13] Trends are considered for the ensemble mean position and strength of the zonal-mean zonal wind-stress for the four reanalyses, 23 CMIP3 and 21 CMIP5 models over the period 1979–2010. The reanalyses and CMIP5 models show no significant trend in annual mean position, while the CMIP3 models show a trend that is marginally significant (Figure 3a). The reanalyses, CMIP3 and CMIP5 models all exhibit their largest trends in the Austral summer (DJF), all of which indicate a poleward shift in the wind-stress, and are statistically significant. However, the significant poleward trend in DJF is counteracted in all cases by an equatorward trend in JJA (and SON in the reanalyses, but not the climate models). No significant annual-mean positional trends appear on a longitude-by-longitude basis (not shown).

Figure 3.

Historical trends in the SH surface westerly wind-stress (a) position and (b) strength. Trends are computed over the period 1979–2010 on the ensemble mean position and strength from four reanalysis products, 23 CMIP3 models and 21 CMIP5models respectively. The error bars show the 95% confidence interval of the trends, where auto-correlation has been accounted for. For each ensemble, trends are computed for monthly means, seasonal means and annual means of the zonal-mean zonal wind-stress. For CMIP3 the data are a combination of historical runs (1979–2000) with SRES A1B (2001–2010), and for CMIP5 a combination of historical runs (1979–2005) with RCP4.5 (2006–2010).

[14] The reanalyses and both groups of climate models show significant positive trends in the strength of the annual-mean wind-stress over the historical period (Figure 3b). The significant annual trends result from the positive trends in wind-stress that occur in all seasons. The largest trends in strength occur in DJF in the reanalyses and CMIP5 models, while the CMIP3 models exhibit the greatest strengthening in SON. In general however, the climate models show a strengthening trend that is significantly weaker than the reanalyses indicate, which can be confirmed by checking that the confidence intervals of the trends do not overlap (Figure 3b).

[15] Swart and Fyfe [2012]found that the pre-industrial control SH westerlies in the CMIP3 models were significantly stronger than the Twentieth-Century Reanalysis over the period 1871 to 1900. This is consistent with our current findings that the modern climatological winds over 1979 to 2010 have roughly the same strength in the reanalysis and the CMIP3 models, and that the models underestimate the strengthening of the winds in time relative to the reanalysis. Note that we have not included the NASA MERRA and NCEP CFSR reanalyses that exhibit negative strength trends, which may be related to discontinuities associated with the assimilation of ocean surface winds (seeauxiliary material).

[16] It should also be noted that wind-stress strength trends will be sensitive to the form of the drag-coefficient employed in the climate models and reanalyses. To compare our results to wind-speed we have computed trends in surface wind-speed over the historical period from 1979 to 2010 in reanalyses and the CMIP5 models (seeauxiliary material). As for surface wind-stress, we find significant positive trends in annual mean surface wind speed in both reanalyses and the CMIP5 models. These positive trends in wind-speed are also consistent with station based trends from Southern Ocean islands [Hande et al., 2012; Yang et al., 2007].

3.3. Projected Changes Over the 21st Century and Sensitivity to CO2 Forcing

[17] The change in the CMIP5 ensemble mean position and strength of the SH westerlies over the 21st century is computed as an anomaly from the 1979–2010 climatology for the four RCP scenarios (Figures 4a and 4b). The changes are greatest under the strongest CO2 forcing in RCP8.5 as expected, with a poleward shift of around 1.5° and a strengthening of 0.02 Pa or roughly 10% by the end of the 21st century.

Figure 4.

Simulated changes in the annual mean SH surface westerly wind-stress (a, c) position and (b, d) strength. The CMIP5 ensemble mean jet position and strength anomalies under historical (1979–2005) and four RCP forcing scenarios (2006–2100) are shown in Figures 4a and 4b; The CMIP3 and CMIP5 ensemble mean jet position and strength anomalies under a 1%/year increase in atmospheric CO2from 286 ppm to doubling are shown in Figures 4c and 4d. Envelopes represent the 95% confidence intervals. Anomalies are relative to the 1979–2010 base-period (Figures 4a and 4b) and 1860–1865 (Figures 4c and 4d), and are computed from the zonal-mean zonal wind-stress. Data have been smoothed with a 5–year wide boxcar.

[18] As in the trends over the historical period, the changes in jet strength over the 21st century are more robust than changes in jet position. A statistically significant change in annual mean position occurs only under RCP8.5 by 2100. Changes in jet strength are significant by the century's end for all the RCPs, except for in RCP2.6, under which significant mitigation of anthropogenic emissions occurs. We note that the changes in jet strength and position over the 21st century in the RCPs are the result of opposing trends in CO2 and ozone forcing. Increasing CO2 forcing drives the jet to strengthen and move polewards, while recovery of stratospheric ozone drives the jet to weaken and shift equatorward [Son et al., 2010]. Therefore ozone recovery over the 21st century may help to explain the weak trends seen in RCP2.6 and 4.5. The changes in the jet projected by the CMIP3 ensemble under the SRES A2 and SRES A1B scenario fall within the envelope of the RCP projections by the CMIP5 ensemble (Figure S4 in Text S1). However, the anthropogenic forcing of the CMIP3 and CMIP5 models during the 21st century differs under the SRES scenarios and RCPs respectively.

[19] To directly compare the relative sensitivity of the multi-model ensembles to CO2 forcing we use the simulations with a 1% a year increase in atmospheric CO2 concentration. The simulations begin at an atmospheric CO2 concentration of roughly 286 parts per million (ppm), as observed around the year 1860, and increase at 1% a year to doubling which occurs 70 years later. The CMIP3 and CMIP5 models both show a continuous poleward shift and strengthening of the SH surface westerly jet in response to this CO2 forcing (Figures 4c and 4d). The ensemble mean response of the jet position and strength to CO2 forcing is very similar in both the CMIP3 and CMIP5 models, and statistically indistinguishable from one another over the 70 year period to CO2 doubling.

4. Summary and Conclusions

[20] Changes in the SH surface westerly wind-stress have been associated with changes in the Agulhas leakage [Biastoch et al., 2009], observed warming of the subtropical western boundary currents [Wu et al., 2012], and with frontal shifts and enhanced warming observed in the Southern Ocean [Gille, 2008], with implications for the Southern Ocean carbon sink [Le Quéré et al., 2007; Zickfeld et al., 2008]. We have shown that while a significant strengthening of the zonal-mean SH westerly wind-stress jet has occurred since 1979, there is no consistent evidence for an annual mean shift in the position of the jet over the historical period. However, poleward shifts have occurred during the Austral summer, which is also the season of greatest strengthening. The large changes during the Austral summer are consistent with previous findings, and owe to stratospheric ozone depletion [Son et al., 2010]. Increasing CO2forcing operates year-round, and also drives the jet to strengthen and move poleward.

[21] The magnitude of the strengthening trend in the CMIP3 and CMIP5 climate models is significantly smaller than the observed strengthening. Though this result should be interpreted with caution because there is disagreement on the strength trends amongst the reanalyses (see auxiliary material); lack of observations leads the reanalyses to be unreliable in the Southern Hemisphere [Son et al., 2010], and the lack of atmosphere–ocean coupling in reanalyses with prescribed SST's could influence changes in the jet.

[22] Wind-stress biases in climate models are expected to have a significant influence on their simulated ocean overturning circulations, and carbon cycles [Swart and Fyfe, 2012]. The CMIP5 climate models show less model-to-model variability, and a more accurate position for the SH surface westerly wind-stress than the CMIP3 models, but they remain significantly equatorward biased relative to the reanalyses over the historical period. The CMIP5 ensemble also suggests that the SH surface westerly wind-stress maximum will move poleward by as much as 1.5° and strengthen by as much as 10% by the end of the 21st century in response to anthropogenic forcing. However, the amount of projected strengthening could be an underestimate, since the CMIP5 ensemble significantly underestimates the strengthening of the wind-stress over the historical period relative to reanalyses.

[23] Changes in the SH winds will have significant implications for the ocean circulation and the carbon cycle in the 21st century [Zickfeld et al., 2007]. As the coupled climate models evolve into Earth System Models, accurate simulation of the SH wind-stress and its time-evolution becomes vital for robust simulations of climate change, and particularly for the diagnosis of carbon emissions, indicating the SH wind-bias as a key model shortfall requiring attention.


[24] N.C.S. was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) CREATE Training Program in Interdisciplinary Climate Science at the University of Victoria (UVic); A UVic Dr. Arne H. Lane Graduate Fellowship in Marine Sciences and the South African National Research Foundation. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison and the WCRP's Working Group on Coupled Modelling for their roles in making available the WCRP CMIP multi-model datasets. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. We thank Nathan Gillett and John Scinocca for helpful comments on the paper, and Robin Rong for help in retrieving CMIP5 data.

[25] The Editor thanks the two anonymous reviewers for assisting in the evaluation of this paper.