Abstract

Westerly wind trends at 850 hPa over the Southern Ocean during 1979-2011 exhibit strong regional and seasonal asymmetries. On an annual basis, trends in the Pacific sector (40°-60°S, 70°-160°W) are 3 times larger than zonal-mean trends related to the increase in the southern annular mode (SAM). S... Show moreWesterly wind trends at 850 hPa over the Southern Ocean during 1979-2011 exhibit strong regional and seasonal asymmetries. On an annual basis, trends in the Pacific sector (40°-60°S, 70°-160°W) are 3 times larger than zonal-mean trends related to the increase in the southern annular mode (SAM). Seasonally, the SAM-related trend is largest in austral summer, and many studies have linked this trend with stratospheric ozone depletion. In contrast, the Pacific sector trends are largest in austral autumn. It is proposed that these asymmetries can be explained by a combination of tropical teleconnections and polar ozone depletion. Six ensembles of transient atmospheric model experiments, each forced with different combinations of time-dependent radiative forcings and SSTs, support this idea. In summer, the model simulates a positive SAM-like pattern, to which ozone depletion and tropical SSTs (which contain signatures of internal variability and warming from greenhouse gasses) contribute. In autumn, the ensemble-mean response consists of stronger westerlies over the Pacific sector, explained by a Rossby wave originating from the central equatorial Pacific. While these responses resemble observations, attribution is complicated by intrinsic atmospheric variability. In the experiments forced only with tropical SSTs, individual ensemble members exhibit wind trend patterns that mimic the forced response to ozone. When the analysis presented herein is applied to 1960-2000, the primary period of ozone loss, ozone depletion largely explains the model’s SAM-like zonal wind trend. The time-varying importance of these different drivers has implications for relating the historical experiments of free-running, coupled models to observations. Show less