Interdecadal and Interannnual Variabilities of the Antarctic Oscillation ...

ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2014, VOL. 7, NO. 6, 515520

Interdecadal and Interannnual Variabilities of the Antarctic Oscillation Simulated by CAM3 XUE Feng1, SUN Dan2,3, and ZHOU Tian-Jun2 1

The International Center for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 3 Beijing Meteorological Bureau, Beijing 100089, China Received 2 April 2014; revised 24 May 2014; accepted 10 June 2014; published 16 November 2014

Abstract Based on four sets of numerical simulations prescribed with atmospheric radiative forcing and sea surface temperature (SST) forcing in the Community Atmospheric Model version 3 (CAM3), the interannual and interdecadal variabilities of the Antarctic oscillation (AAO) during austral summer were studied. It was found that the interannual variability is mainly driven by SST forcing. On the other hand, atmospheric radiative forcing plays a major role in the interdecadal variability. A cooling trend was found in the high latitudes of the Southern Hemisphere (SH) when atmospheric radiative forcing was specified in the model. This cooling trend tended to enhance the temperature gradient between the mid and high latitudes in the SH, inducing a transition of the AAO from a negative to a positive phase on the interdecadal timescale. The cooling trend was also partly weakened by the SST forcing, leading to a better simulation compared with the purely atmospheric radiative forcing run. Therefore, SST forcing cannot be ignored, although it is not as important as atmospheric radiative forcing.  Keywords: Antarctic oscillation, interannual variability, interdecadal variability Citation: Xue, F., D. Sun, and T.-J. Zhou, 2014: Interdecadal and interannual variabilities of the Antarctic Oscillation simulated by CAM3, Atmos. Oceanic Sci. Lett., 7, 515–520, doi:10.3878/AOSL20140036.

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Introduction

The Antarctic oscillation (AAO) is a seesaw pattern in sea level pressure between the mid and high latitudes of the Southern Hemisphere (SH), characterized by an approximately zonal symmetry and an equivalent barotropic structure in the vertical direction (Gong and Wang, 1999). The AAO, which is also distinguishable in some other variables such as zonal wind and temperature, is also referred to as the southern annular mode (Thompson and Wallace, 2000). As a dominant component of SH atmospheric circulation, the AAO plays a crucial role in climate anomalies over the vast region of the SH. Rainfall anomalies in South America and South Africa, for instance, are closely related with the AAO’s phase (Silvestri and Vera, 2003; Reason and Rouault, 2005). During boreal summer, Corresponding author: XUE Feng, [email protected]

warm pool convective activity and typhoons in the western tropical Pacific are influenced by the AAO through cross-equatorial flows, further inducing rainfall anomalies in the East Asian monsoon region via the East AsianPacific teleconnection pattern (Xue et al., 2004; Wang and Fan, 2007). It is widely accepted that the AAO owes its existence to internal atmospheric dynamics, maintained by a positive feedback of transient eddies upon zonal wind (Karoly, 1990). On the other hand, interactions with the surface ocean, especially the El Niño-Southern Oscillation (ENSO), may also contribute to its interannual variability. As a response to the anomalous convective heating in the tropics induced by ENSO, the Pacific-South American pattern may transfer the ENSO signal to the high latitudes of the SH (Sun et al., 2013a). Besides, ENSO may play a role in the phase transition of the AAO through the eastward propagation of the global tropical wave (Liu and Xue, 2010). The numerical experiment by Zhou and Yu (2004) demonstrated that the interannual variability of the AAO is significantly forced by ENSOrelated sea surface temperature (SST) anomalies in the tropical Pacific. Besides interannual variability, the AAO exhibits a significant interdecadal variability. At the end of the 1970s, the sea level pressure and wind field in the mid and high latitudes of the SH underwent a pronounced change, characterized by a deepening of the circumpolar lows and a strengthening of sea level pressure in the midlatitudes. As a result, the AAO has exhibited a trend towards positive polarity over the past few decades, with the largest and most significant trend observed during the summer months of the SH (Thompson and Soloman, 2002). Observational and modeling studies have shown that photochemical ozone depletion has had a distinct impact on the positive trend in the AAO (Gillett and Thompson, 2003). It has also been noted that the influence of the circulation in the SH on the East Asian summer monsoon has tended to intensify in association with the positive trend of the AAO (Sun et al., 2013b). The aforementioned studies tended to focus on one factor, such as ozone depletion. Less attention has been paid on the combined effects of all possible factors on the AAO’s variability. In the present study, we employed four sets of ensemble runs under different forcings with the

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Community Atmospheric Model version 3 (CAM3) to further elucidate the influence of different factors on the AAO at both the interannual and interdecadal timescales. Considering that both the trend and the impact of ENSO on the AAO are most significant during the summer months in the SH (Thompson and Soloman, 2002; Zhou and Yu, 2004), we focused our analysis on the December-January-February (DJF) period.

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Model and data

The model used in this study was the National Center for Atmospheric Research (NCAR) CAM3, which employs a Eulerian dynamic core on a T42 (approximately 2.8125°) grid and 26 vertical levels (Collins et al., 2006). Four sets of ensemble runs under different forcings from 1950 to 2000 were performed with CAM3. The global ocean global atmosphere plus Intergovernmental Panel Climate Change 20th century forcing (GOGAI) runs (all forcing runs) were forced by observed global SSTs plus historical evolution of atmospheric forcing agents, including observed greenhouse gases, aerosols, tropospheric and stratospheric ozone, and solar irradiance. The forcing data were obtained from NCAR Coupled Model Intercomparison Project Phase 3 (CMIP3) experiments (Li et al., 2010). The global ocean global atmosphere (GOGA) runs were forced by the historical global SSTs with fixed atmospheric forcings (set to the 1990 level). The tropical ocean global atmosphere (TOGA) runs were forced by the time-varying tropical (20°S–20°N) SSTs and fixed climatological SSTs (with seasonal cycle) polewards of 30° latitude, with linear interpolation between 20° and 30° latitude. The atmospheric forcings were fixed at the 1990 level in the TOGA runs. The atmospheric radiation forcing (RADATM) runs were forced by climatological monthly SSTs and the time-varying atmospheric forcings during the period 1950–2000. The SST dataset used for forcing the model was a blended version of the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and Reynolds datasets (Hurrell et al., 2008). Only the direct radiative effect of aerosols was considered in these model runs. In addition, both National Centers for Environmental Prediction (NCEP) reanalysis data (Kalnay et al., 1996) and European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA-40; Uppala et al., 2005) of atmospheric circulation were used for comparison with the model simulations. It should be noted that ERA-40 data are only available during the period 1958–2000.

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lation can be seen between the Antarctic region and the midlatitudes. The AAO pattern was generally well reproduced in the four sets of model runs (Figs. 1c–f), with a higher spatial correlation coefficient (CC) in the RADATM and GOGAI runs (Figs. 1c and 1d). Note that there was also a negative correlation region in the tropics of the Eastern Hemisphere in the GOGA and TOGA runs (Figs. 1e and 1f), which was somewhat different from the NCEP and ERA-40 data (Figs. 1a and 1b). The EOF1 mode explained 50.9% (38.3%) of the total variance in the NCEP (ERA-40) data. The variance explained by EOF1 was 75.3% in the RADATM run, 58.4% in the GOGAI run, 49.6% in the GOGA run, and 54.8% in the TOGA run. Clearly, the inclusion of SST forcing reduced the variance contribution of atmospheric forcing agents, i.e., there is competition between SST forcing and atmospheric forcing agents in driving the long-term changes of the AAO. The corresponding time series of EOF1 in Fig. 1 is shown in Fig. 2. Besides a robust interannual variability, the AAO exhibited an evident interdecadal change in both the NCEP and ERA-40 data, characterized by a transition from a negative phase to a positive one in the late 1970s

Interdecadal variability

Empirical orthogonal function (EOF) analysis was used to derived the AAO mode. Figure 1 shows the first component of 700 hPa geopotential height south of 20°S during DJF by EOF analysis (EOF1), which is often used as a proxy of AAO index (Thompson and Soloman, 2002). A clear AAO pattern was evident in both NCEP and ERA-40 data (Figs. 1a and 1b), i.e., a negative corre-

Figure 1 The first component of 700 hPa geopotential height south of 20°S by EOF in December-January-February. The value above each subfigure indicates the spatial correlation coefficient with (a) NCEP data.

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Figure 2 The corresponding time series of EOF1 in Fig. 1. The red line indicates the 11-yr running mean, and the values above each subfigure indicate the variance explained by EOF1 and the correlation coefficient with (a) NCEP data.

(Figs. 2a and 2b). The linear trend was 0.45 per decade in the NCEP data and 0.33 per decade in the ERA-40 data, both of which were statistically significant at the 0.05 level. The RADATM and GOGAI runs generally simulated the interdecadal trend with a more significant trend in the RADATM run, indicating that atmospheric radiative forcing plays a critical role (Figs. 2c and 2d). The linear trend was 0.50 per decade in the RADATM run and 0.36 per decade in the GOGAI run. In contrast to the RADATM and GOGAI runs, a negative trend appeared in the GOGA and TOGA runs (Figs. 2e and 2f), which was also opposite to the NCEP and ERA-40 data. Hence, SST forcing was not the primary factor responsible for the enhanced AAO trend of recent decades. However, in comparison with the RADATM run (Fig. 2c), the trend of 0.36 per decade in the GOGAI run with SST forcing tended to weaken, bringing it closer to that in the NCEP and ERA-40 data. This was also indicated by the CCs of 0.483 and 0.475 in the GOGAI and RADATM runs, respectively. Hence, in contrast to some previous studies (e.g., Gillett and Thompson, 2003), we found that the effect of SST forcing on the AAO trend cannot be entirely neglected, although it is not as important as the atmospheric forcing agents. To further understand why the specified atmospheric radiative forcing can drive long-term changes of the AAO, Fig. 3 shows the latitude-height cross-section of

mean air temperature difference between 1977–2000 and 1950–1976. Except for a cooling in the high latitudes at upper levels, a consistent warming trend was found over most of the globe in both the NCEP and ERA-40 data (Figs. 3a and 3b). The central warming was greater than 1.2°C in the reanalysis data. However, a remarkable discrepancy was found in the high latitudes of the SH between the two reanalysis datasets. Marshall (2003) pointed out that there is a serious error in NCEP data with respect to the high latitudes of the SH before 1968, while ERA-40 data provide a reasonable trend that can be used with high confidence. As shown in Fig. 3b, based on ERA-40 data, a cooling trend at upper levels and a warming trend in the lower troposphere was found in the high latitudes of the SH. The central value of the cooling (warming) trend exceeded −1.8°C (1.2°C) during the period 1958–2000. In the GOGA and TOGA runs that were driven only by historical SST changes (Figs. 3e and 3f), a warming trend was found over most parts of the globe, especially in the tropics, which was significantly different from the observed trend shown in Fig. 3b. After adding atmospheric forcing in the RADATM and GOGAI runs (Figs. 3c and 3d), an evident cooling trend appeared in the upper levels at high latitudes of the SH. The central value of the cooling trend was −0.9°C. The temperature gradient tended to increase under the combined effect of high-latitude cooling and mid-latitude warming, resulting in a transition of the AAO from a negative to positive phase.

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Figure 3 Latitude-height cross-section of mean air temperature difference between 1977–2000 and 1950–1976. The difference in (b) ERA-40 data is between 1977–2000 and 1958–1976. Regions above the 95% confidence level are dotted (units: °C).

On the other hand, the cooling trend in the lower levels at high latitudes in the SH in the RADATM run was partly suppressed by the SST forcing (Figs. 3c and 3d). As a result, the trend in the GOGAI run agreed better with the observation than that in the RADATM run. This result is similar to the correlation analysis shown in Fig. 2. Thus, SST forcing also plays a role in the interdecadal variability of the AAO, although it is not as important as atmospheric forcing. An examination of the similarities and differences between the reanalysis data and model runs under different scenarios indicated that the observed upper-level cooling trend was dominated by changes of atmospheric forcing agents, but the SST forcing, mainly the tropical ocean forcing, tended to reduce this cooling trend.

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Interannual variability

By removing the interdecadal variation and long-term trend with an 11-year filter, we obtained the interannual variability of the AAO (Fig. 4). A clear AAO pattern stood out in both the NCEP and ERA-40 data, as evidenced by the negative correlation between the Antarctic region and the midlatitudes (Figs. 4a and 4b). Different from Fig. 1, there was a negative region in the tropics of the Eastern Hemisphere. The interannual variability was reasonably reproduced in the four sets of model runs with a reasona-

bly high spatial CC, which was larger than 0.87 and statistically significant at the 0.05 level (Figs. 4c–f). A robust interannual variability was evident in the corresponding time series, as shown in Fig. 5. While a statistically significant CC was evident in the GOGA and TOGA runs (Figs. 5e and 5f), the CC in the RADATM and GOGAI runs was very low and statistically insignificant at the 0.05 level (Figs. 5c and 5d). To further reveal the physical mechanisms underpinning the interannual variability of the AAO, Fig. 6 shows the CCs between the time series and SST in DJF. A clear ENSO pattern was found in both the NCEP and ERA-40 data (Figs. 6a and 6b), with a significantly negative correlation in the tropical eastern Pacific, tropical Indian Ocean, and high latitudes of the SH, and a significantly positive correlation in the midlatitudes of the SH. Hence, the AAO exhibits a negative phase when an El Niño event occurs. The above correlation distribution was reproduced well in the GOGAI, GOGA, and TOGA runs, with a relatively lower correlation in the GOGA run (Figs. 6d–f). By contrast, no significant correlation was found in the RADATM run (Fig. 6c). This result further demonstrated that SST forcing, especially ENSO-related SST anomalies, plays a major role in the interannual variability of the AAO (Zhou and Yu, 2004; Sun et al., 2013b). Thus, the forcing factor of interannual variability of the AAO differs significantly

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from that of interdecadal variability. The EOF1 mode explained 38.3% of the total interannual variance in the NCEP data and 39.3% in the ERA-40 data, while the corresponding variance was 53.1% in the GOGA run and 57.7% in the TOGA run. Hence, it is mainly due to tropical ocean forcing that the AAO exhibits a robust interannual variability. The inclusion of atmospheric forcing may have reduced the interannual variation of the AAO driven by SST changes, as evidenced by the lower CC in the GOGAI run compared to the GOGA run and the correlation distribution shown in Fig. 6c.

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Figure 4 The first component of 700 hPa geopotential height south of 20°S by EOF in December-January-February after the data were pretreated with a 11-yr filter. The value above each subfigure indicates the spatial correlation coefficient with (a) NCEP data.

Summary

Based on four sets of numerical experiments with CAM3, we studied the interannual and interdecadal variability of the AAO in the SH summer. Similar to previous studies, we found the interannual variability of the AAO to be mainly determined by SST forcing, especially ENSO-related SST anomalies in the tropical oceans. On the other hand, atmospheric radiative forcing was found to play a major role in the interdecadal variability of the AAO. In particular, a cooling trend in the high latitudes of the SH in recent decades has tended to enhance the temperature gradient between the mid and high latitudes, inducing a transition of the AAO from a negative to positive phase. It was also found that the cooling trend was suppressed by SST forcing to a certain degree, such that the simulated trend agreed better with the observation. It can be concluded that SST forcing is also important to the interdecadal variability of the AAO. It is important to note that this study was focused on

Figure 5 The corresponding time series of EOF1 in Fig. 4. The values above each subfigure indicate the variance explained by EOF1 and the correlation coefficient with (a) NCEP data.

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Figure 6 The correlation coefficient between the time series in Fig. 5 and SST in December-January-February. Regions above the 95% confidence level are marked with crosses.

the SH summer, when the trend is most significant. However, the result cannot necessarily be extended to other seasons. As shown in the numerical experiment by Gillett and Thompson (2003), there is a large discrepancy during April and May between observed and simulated results, and thus the interdecadal variability during this period is likely due to other influences. Besides, Ding et al. (2012) also noted that, unlike in the SH summer, tropical SST forcing plays a major role in the AAO’s variability in the SH winter. Therefore, it is necessary to conduct further analyses for other seasons. Acknowledgements. The authors appreciated the comments and suggestions from the two anonymous reviewers. This study was jointly supported by the Carbon Budget and Related Issues of the Chinese Academy of Sciences (Grant No. XDA05110201) and the National Basic Research Program of China (Grant No. 2010CB951901).

References Collins, W. D., P. J. Rasch, B. A. Boville, et al., 2006: The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3), J. Climate, 19, 2144–2161. Ding, Q., E. J. Steig, D. S. Battist, et al., 2012: Influence of the tropics on the southern annular mode, J. Climate, 25, 6330–6348. Gillett, N. P., and D. W. J. Thompson, 2003: Simulation of recent Southern Hemisphere climate change, Science, 302, 273–275. Gong, D. Y., and S. W. Wang, 1999: Definition of Antarctic Oscillation index, Geophys. Res. Lett., 26, 459–462. Hurrell, J., J. Hack, D. Shea, et al., 2008: A new sea surface temperature and sea ice boundary data set for the community atmosphere model, J. Climate, 21, 5145–5153. Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/ NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437–171. Karoly, D. J., 1990: The role of transient eddies in low-frequency zonal variations of the Southern Hemisphere circulation, Tellus, 42A, 41–50.

Li, H., A. Dai, T. Zhou, et al., 2010: Responses of East Asian summer monsoon to historical SST and atmospheric forcing during 1950–2000, Climate Dyn., 34, 501–514. Liu, C. Z., and F. Xue, 2010: The relationship between the canonical ENSO and the phase transition of the Antarctic oscillation at the quasi-quadrennial timescale, Acta Oceanol. Sinica, 29, 28–37. Marshall, G. J., 2003: Trends in the southern annular mode from observation and reanalyses, J. Climate, 24, 4134–4143. Reason, C. J. C., and M. Rouault, 2005: Links between the Antarctic oscillation and winter rainfall over western South Africa, Geophys. Res. Lett., 32, doi:10.1029/2005GL022419. Silvestri, G. E., and C. S. Vera, 2003: Antarctic Oscillation signal on precipitation anomalies over southeastern South America, Geophys. Res. Lett., 30, 2115, doi:10.1029/2003GL018277. Sun, D., F. Xue, and T. J. Zhou, 2013a: Impacts of the two types of El Niño events on the atmospheric circulation in the Southern Hemisphere, Adv. Atmos. Sci., 30, 1732–1742. Sun, D., F. Xue, and T. J. Zhou, 2013b: Influence of Southern Hemisphere circulation on summer rainfall in China under various decadal backgrounds, Climatic Environ. Res. (in Chinese), 18, 51–62. Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern Hemisphere climate change, Science, 296, 895–899. Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability, J. Climate, 13, 1000–1016. Uppala, S. M., P. W. Kallberg, A. J. Simmons, et al., 2005: The ERA-40 reanalysis, Quart. J. Roy. Meteor. Soc., 131, 2961–3211. Wang, H. J., and K. Fan, 2007: Relationship between the Antarctic oscillation in the western North Pacific typhoon frequency, Chinese Sci. Bull., 52, 561–565. Xue, F., H. J. Wang, and J. H. He, 2004: Interannual variability of Mascarene high and Australian high and their influences on East Asian summer monsoon, J. Meteor. Soc. Japan, 82, 1173– 1186. Zhou, T., and R. Yu, 2004: Sea-surface temperature induced variability of the Southern Annular Mode in an atmospheric general circulation model, Geophys. Res. Lett., 31, L24206, doi:10.1029/2004GL021473.