Southern Mid- to High-Latitude Variability, a Zonal Wavenumber-3 Pattern, and the Antarctic Circumpolar Wave in the CSIRO Coupled Model

OCTOBER 1999 CAI ET AL. 3087 Southern Mid- to High-Latitude Variability, a Zonal Wavenumber-3 Pattern, and the Antarctic Circumpolar Wave in the CSIRO Coupled Model WENJU CAI, PETER G. BAINES, AND HAL B. GORDON CSIRO Atmospheric Research, Aspendale, Victoria, Australia (Manuscript received 29 June 1998, in final form 14 December 1998) ABSTRACT Variability in the southern atmospheric circulation at mid- to high latitudes with a dominant quasi-stationary wavenumber-3 pattern has been reported in many observational studies. The variability is barotropic in nature with signals in the middle troposphere as well as at the atmosphere ocean interface. Moreover, there are preferred fixed centers for the strongest anomalies. These features are well reproduced by the Commonwealth Scientific and Industrial Research Organisation coupled model on various timescales. On the interannual timescale, an index of the modeled wavenumber-3 pattern shows little correlation with the modeled Southern Oscillation index, suggesting that the variability associated with wavenumber-3 anomalies is separate to modeled ENSO-like events. However, the variation of the pattern index is strikingly similar to, and highly correlated with, the modeled oceanic variability. The associated oceanic anomalies move eastward and are similar to those of the observed Antarctic circumpolar wave (ACW). The modeled ACW-like anomalies exist not only at the surface but also through middle ocean depths, with a similar barotropic nature to those of the atmospheric anomalies. The oceanic anomalies also display a wavenumber-3 pattern. The essential elements of the dynamics of the modeled ACW are the advection of SST anomalies by the surface Antarctic Circumpolar Current (ACC), and the interactions between anomalies of SST and mean sea level pressure (MSLP). Associated with the standing wavenumber-3 pattern, there are fixed centers for the strongest MSLP anomalies. As a positive SST anomaly advected by surface ACC approaches a center of a positive MSLP anomaly, the MSLP decreases. The positive (negative) SST anomalies are generated by anomalous latent and heat fluxes, which are in turn induced by southward (northward) meridional wind stress anomalies resulting from geostrophic balance. These MSLP anomalies change sign when the positive (negative) SST anomalies move to a location near the centers. Once MSLP anomalies change sign, positive (negative) SST anomalies are generated again reinforcing the anomalies entering from the west. The time for the surface ACC to advect one-sixth of the circuit around the pole corresponds to the time of a half-cycle of the standing MSLP oscillations. Thus the surface ACC determines the frequency of the standing oscillation. In the present model, the speed of the surface ACC is such that the period of the standing oscillation is 4 5 yr, and it would take 12 16 yr for an anomaly to encircle the pole. These and other features of the modeled ACW, together with associated dynamic processes, are analyzed and discussed. 1. Introduction The variability of the southern atmospheric circulation has not been studied as extensively as its northern counterpart, and investigations have been based on observations [see Karoly (1995) for an overview]. One observational feature is a dominant quasi-standing wavenumber-3 oscillating pattern in the mid- to high southern latitudes. This feature is dominant on a wide range of timescales, ranging from day-to-day (e.g., Wallace and Hsu 1983; Mo 1986; Mo and Ghil 1987; Kidson 1988) to interseasonal (e.g., Mo and White 1985; Karoly et al. 1989; Mo and Higgins 1999, manuscript submitted Corresponding author address: Dr. Wenju Cai, CSIRO Atmospheric Research, PMB1, Aspendale, Victoria 3195, Australia. E-mail: wjc@dar.csiro.au to Mon. Wea. Rev.) and to interannual (Mo and van Loon 1984; Karoly et al. 1989). In particular, Mo and White (1985) (hereafter MW) demonstrated that there are preferred locations (centers) for the strongest anomalies in the 500-mb geopotential height (hereafter referred to as Z) and mean sea level pressure (MSLP). The effect of the wavenumber-3 pattern on, and its interaction with, the variability of the Southern Ocean has not previously been explored. This is one of the major objectives of this study and will be carried out by using results from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) coupled model. Recently, an interactive phenomenon in the southern mid- to high latitudes has been observed with eastward propagating signals in both oceanic and atmospheric variables. This has been termed the Antarctic circumpolar wave (ACW) by White and Peterson (1996; hereafter WP). In the WP study, several well-defined fea- 1999 American Meteorological Society

3088 JOURNAL OF CLIMATE VOLUME 12 tures appear. First, local signals in fields of sea surface temperature (SST), MSLP, meridional wind stress, and sea ice extent are generated every 4 5 yr. Second, these signals are phase locked and move eastward, encircling the globe in about 8 10 yr. Third, at any instant there is a predominant wavenumber-2 spatial pattern. White and Peterson hypothesized that there exists a teleconnection between the ACW and El Niño Southern Oscillation (ENSO) events. In a separate study, Jacobs and Mitchell (1996) analyzed altimeter data and found that sea surface anomalies vary with the ACW with a wavenumber-2 pattern. Subsequently, ACW-like phenomena have been found in coupled ocean atmosphere models (Christoph et al. 1998, hereafter CBR; Motoi et al. 1998). In the CBR study, the eastward movement appears in the SST anomalies but not in other aforementioned fields. In those fields the signal is dominated by standing oscillations and there is no phase locking. Although local signals are generated every 4 5 yr as in the observations (WP), the SST signal takes 12 16 yr to complete a circuit around the pole, and there is a predominant wavenumber-3 pattern. CBR examined the possibility of a teleconnection between ACW and ENSO events in their model, as proposed in the WP study, but found that ENSO does not play a significant role. They concluded that the ACW has its origins in mid- to high latitudes. However, the dynamics of their modeled ACW and the interaction between SST anomalies and the atmospheric anomalies are not clear. The results from the study by Motoi et al. (1998) show propagating signals in the SST together with standing oscillations in other fields, but in their model there is a preferred wavenumber-2 pattern, and the SST anomalies would take 20 30 yr to complete a circuit. Motoi et al. (1998) proposed that the background current is important in advecting the anomalies eastward, and the slower propagation in their model was due to a weak Antarctic Circumpolar Current (ACC). They argued that their modeled ACW is closely related to ENSO activities and that saline forcing is an indispensable component, but no mechanism was proposed. In an attempt to provide a mechanism, Weisse et al. (1997) used an ocean-only general circulation model (GCM) to assess whether the observed ACW can be partially explained by a simple oceanic response to atmospheric stochastic forcing. They added spatially coherent stochastic components of heat, freshwater, and momentum flux to an oceanic forcing climatology to account for short-term atmospheric fluctuations. These components were derived from an atmosphere-only GCM experiment. The ocean-only GCM produced pronounced variability on a timescale of 6 yr. The variability is characterized by upper-level anomalies propagating eastward along the ACC at the mean velocity. With a wavenumber-3 pattern, they were able to reconstruct the basic feature of the modeled ACW in CBR. The present study demonstrates that the observed dominance of the wavenumber-3 pattern in the southern atmospheric anomalies (e.g., MW; Mo and van Loon 1984) is well simulated by the CSIRO mark II coupled model. On the interseasonal timescale (May September), the spatial pattern compares well with that in the observations as reported in the MW study. When variability on timescales shorter than one year is suppressed, the spatial-zonal wavenumber-3 pattern persists with the same preferred fixed centers as in the winter anomalies, and the dominant timescale is 4 5 yr (section 3). Similar results appear in the oceanic variability with an interannual timescale of 4 5 yr and a dominant wavenumber-3 pattern. These oceanic signals appear not only on the surface, but also in the interior thermohaline fields. As in the observations, the oceanic anomalies move eastward, but they take 12 16 yr to encircle the pole. Although the strength of the modeled ACC decreases with depth, the time for these oceanic anomalies to encircle the pole appears to be independent of depth (section 4). It is shown that a wavenumber-3 pattern index constructed from the interannual atmospheric variability corresponds remarkably well with the leadingmode temporal coefficients of empirical orthogonal function (EOF) analysis of the oceanic variability. This interannual oceanic and atmospheric variability is referred to as the modeled ACW. Its dynamics and the interactions between various modeled ACW components are then explored (section 5). 2. The CSIRO coupled model A detailed description of the CSIRO global coupled model is given by Gordon and O Farrell (1997). The model consists of atmospheric, oceanic, sea ice, and biospheric components, and has full annual and diurnal cycles. The spectral, 9-level, atmospheric GCM is an updated version of the CSIRO atmospheric GCM described by McGregor et al. (1993). The gridpoint, 12- level, oceanic GCM is a version of the Geophysical Fluid Dynamics Laboratory model based on Bryan (1969). The horizontal resolution of the coupled model is that of the spectral atmospheric GCM (R21), which has a grid resolution of about 3.2 lat 5.6 long. In order to prevent a drift of the coupled-model state from the realistic precoupling spin-up states, the coupled model is flux adjusted and the method of adjusting fluxes is described in Gordon and O Farrell (1997). The adjustments are kept constant (with a seasonal cycle) in subsequent coupled-model experiments. The coupled control run starts from year 56 (the atmospheric model spin-up had a final year number of 55). As in other flux-adjusted coupled models, despite the use of flux adjustments, some small drift following coupling still takes place (Cai and Gordon 1999). By year 200, the initial drift had largely settled down, and so the model fields from year 200 to year 260 are used.

OCTOBER 1999 CAI ET AL. 3089 3. Zonal wavenumber-3 patterns in the Southern Hemisphere atmospheric anomalies As discussed in the introduction, many observational studies have revealed the dominance of wavenumber-3 standing oscillations in the variability of the mid- to high latitudes of the southern atmosphere. To examine the presence of this dominance in the CSIRO coupled model, and to enable a direct comparison between modeled and observed variability, an analysis similar to that by MW has been performed using monthly mean anomalies of MSLP and Z. As in their (MW) study, a separate analysis is carried out for winter (May September) and summer (November March) seasons. One-point correlations are calculated between winter monthly anomalies at a base point of 49.3 S, 95.6 E and anomalies at other grid points. The base point is the model grid point closest to their selected point. The results for the winter season are shown in Figs. 1a and 1b. For the 5-month winter anomalies, a similar zonal wavenumber-3 pattern appears in both the Z and MSLP, each showing three centers of positive correlations. The three centers (including the chosen one) for Z (x i, y i, i 1, 3) are 95.6 E, 49.3 S; 140.2 W, 46.2 S; and 39.8 S, 16.9 W. For MSLP the first and second centers are at the same locations, but the third moves to 36.6 S, 11.2 W. These locations are close to those identified by MW, although the latitude of the second center in our model is about 10 closer to the equator. In our study the positive correlation coefficients at the second and third centers (away from the base point) are smaller relative to theirs, but it should be noted that our sample is nearly eight times larger. We have used 60 yr (a total of 300 samples) of winter data whereas they only used 8 yr of data (a total of 40 samples). These coefficients increase to comparable values when only 8 yr of model data are used, with the wavenumber-3 pattern being reproducible using different 8-yr datasets. In these analyses using 8-yr data, the correlation coefficients at the centers are at least 0.45. This value is greater than 0.4, which corresponds to a 99% significance level. As in the observations, the wavenumber-3 pattern has a preferred geographical location, and this pattern disappears when the base point is moved away by more than about 10 in either latitudinal or longitudinal direction. Thus there is a similar dominance of a wavenumber-3 pattern on the interseasonal timescale in our model, which corresponds to atmospheric observations. The feature of a wavenumber-3 pattern is further confirmed by an EOF analysis with maximum variability at the three centers. However, in order to enable a direct comparison with results from MW, our analysis was conducted in ways similar to MW. Following MW, a pattern index called wavenumber-3 index I 3 is formed by compositing individual monthly fields from normalized anomalies (Z*) at the three centers for both the Z and MSLP. Here Z* (Z Z)/, where Z is the climatological monthly mean and is FIG. 1. Correlation of monthly winter (May Sep) anomalies with anomalies at 49.3 S, 95.6 E over the 60-yr period for (a) 500-mb geopotential height (Z), and for (b) mean sea level pressure (MSLP). Contour interval is 0.1. Positive values are shaded. the standard deviation of the monthly mean for a given season. Here I 3 is defined as 1 I3 [Z*(x 1, y 1) Z*(x 2, y 2) Z*(x 3, y 3)]. 3 The time series of I 3 for both MSLP and Z for the winter season are plotted in Fig. 2. A positive index denotes anomalously high Z and MSLP, and vice versa. These two time series are highly correlated and the correlation coefficient is 0.89. As in the observations, this reflects

3090 JOURNAL OF CLIMATE VOLUME 12 FIG. 2. The wavenumber-3 index I 3 Z and MSLP for winter. the equivalent barotropic vertical structure of the wavenumber-3 pattern. The barotropic feature is further confirmed by regressing unfiltered Z and MSLP anomalies onto the MSLP I 3, and the results are displayed in Fig. 3. It is seen that there are corresponding centers for maximum variability in both Z and MSLP. In order to compare the magnitude of this variability with the observed, composites of anomalies are then constructed for the strongest positive I 3 (peak) and the strongest negative (trough) values. Shown in Fig. 4 are the Z composites for (a) the strongest positive MSLP I 3, (b) the strongest negative MSLP I 3, and (c) the difference [(a) (b)]. When compared with Fig. 6c of MW, it is found that the amplitude of the model variability is about 140 m for the Z, which is comparable to the observed amplitude of about 135 m. In the analysis of MW, the variations of MSLP and 500 mb in summer over the subtropics of the three continents are both in phase but are out of phase with those over the subtropical oceans in a zonal wavenumber-3 pattern over the southern midlatitudes. Similar results were seen in the Karoly (1989) study. Mo and White demonstrated that this pattern has a strong teleconnection with ENSO and is highly correlated with a Southern Oscillation index. In the present study a similar analysis has been carried out for the summer season. However, such a correspondence was not obtained. Instead, a zonally symmetric pattern is prevalent. Thus the observed teleconnection between the summer anomalies and ENSO has not been reproduced by our model. This is not surprising given that in this model, ENSO-like anomalies are much weaker than observed (Gordon and O Farrell 1997). Another aspect that has been considered is whether the dominance of wavenumber-3 pattern persists on an interannual timescale. We correlate the unfiltered full monthly (12 months per year) with anomalies at the identical base point filtered retaining variability with timescales between 1 and 10 yr. Unless stated otherwise, full 12-month anomalies are used for examining interannual variability and are filtered so that only variability on timescales of 1 10 yr is retained. The correlation maps for Z and MSLP are depicted in Fig. 5. It is seen that on the interannual timescale the dominance of zonal wavenumber 3 still persists. The centers of strongest positive correlations for each field are the same as in Fig. 1. To determine the dominant frequency, a wavenumber-3 index I 3ann (ann denotes interannual timescale) for each filtered field is constructed and plotted in Fig. 6. It is seen that the dominant timescale is 4 5 yr, and this timescale is confirmed by a spectral analysis. The two I 3 time series share similar temporal variations and

OCTOBER 1999 CAI ET AL. 3091 FIG. 3. Spatial patterns generated by regressing the unfiltered (a) winter Z anomalies and (b) winter MSLP anomalies onto the MSLP I 3 index. Contour interval is 10 m and 1 mb per standard deviation of the MSLP I 3 index. FIG. 4. Composites of Z anomalies (m) for (a) strongest positive (peak) and (b) strongest negative (trough) values of the MSLP wavenumber-3 index. Shown in (c) is the difference between (a) and (b). Contour interval is 10 for (a) and (b) and 20 for (c). Positive values are shaded.

3092 JOURNAL OF CLIMATE VOLUME 12 for (a) the strongest positive, (b) the strongest negative I 3ann of the MSLP anomalies, and (c) the difference [(a) (b)]. It is seen that over a region with an anomalously high MSLP to the west and an anomalously low MSLP to the east, there is a northward (positive) meridional wind stress anomaly, and vice versa. As will be demonstrated in section 5, these form part of the atmospheric manifestations of the modeled ACW. In order to further examine any possible teleconnection between variability on this timescale and that associated with modeled ENSO-like variability, a Southern Oscillation index is computed. The index is calculated as the normalized difference in MSLP between Tahiti and Darwin (Tahiti minus Darwin). A correlation analysis between the two time series produced a correlation coefficient that is nearly zero. We have also constructed a NINO3 SST index. Again there is virtually no correlation between this and the I 3ann. Thus in this model the interannual variability in the mid- to high southern latitude with a dominant wavenumber-3 pattern is not related to modeled ENSO-like variability. FIG. 5. Correlation of unfiltered monthly anomalies of (a) Z and (b) MSLP with filtered (retaining 1 10 yr) anomalies at 49.3 S, 95.6 E. Contour interval is 0.1. Positive values are shaded. have a correlation coefficient of 0.86, which reflects the equivalent barotropic nature of the variability on this timescale. Regressing unfiltered Z and MSLP anomalies onto the MSLP I 3 index again confirms this barotropic feature (figure not shown), with three corresponding centers for maximum variability in both Z and MSLP. Following from geostrophic balance, the wavenumber-3 pattern in MSLP and in Z implies that the variability in the surface meridional wind stress will also display a corresponding pattern. Figure 7 shows the composites of the anomalies of meridional wind stress 4. Modeled oceanic interannual variabilities a. Eastward movement of thermohaline anomalies As in the case of the atmospheric interannual variability in section 3, the full 12-month oceanic anomalies are bandpass filtered, retaining variability with timescales of 1 10 yr. The filtered surface thermohaline anomalies show an eastward movement as in the observations of WP (see section 5), in the modeling studies of CBR, and in Motoi et al. (1997). This eastward movement is also evident in the anomalies in the thermohaline fields below the surface. Figure 8 shows time longitude diagrams of temperature and salinity anomalies (meridionally averaged 45 65 S) at level 5 (215 m). The eastward propagation of the temperature signal is also clearly displayed down at level 8 (1025 m; Fig. 9a), as well as in the volume-mean (over the whole oceanic depth) temperature anomalies (Fig. 9b). Thus the signals are also present at the ocean interior. To determine whether thermal or saline anomalies are dominant, anomalies of potential density, together with those components attributable to temperature and salinity anomalies, are calculated. The density anomalies due to temperature anomalies are calculated using a seasonal climatology of salinity (obtained over the 60-yr period) and filtered temporal temperature fields. Such calculations enable direction comparisons to be made between salinity and temperature anomalies in terms of a common quantity, potential density. Those for level 5 are illustrated in Fig. 10. It is seen that thermal anomalies dominate saline anomalies, that positive temperature anomalies are associated with positive salinity anomalies, and that the salinity anomalies act to weaken the effect of temperature anomalies. As a result of the thermal dominance, the pattern of the evolution of the total density anom-

OCTOBER 1999 CAI ET AL. 3093 FIG. 6. The wavenumber-3 index I 3ann for Z and MSLP anomalies. alies (Fig. 10a) is similar to the thermally induced density anomalies (Fig. 10b). The thermal dominance suggests that the anomalies may be induced by surface air sea heat flux anomalies. The surface heat flux has been examined to determine which component of the heat flux is the major source of the anomalies. The net heat flux anomaly into the ocean dq ao is given by dq ao dsw in dlw out dlh dsh dhsi. Here dsw in is the anomalous net shortwave radiation gain by the ocean, and the remaining terms are anomalous heat loss due to, respectively, net longwave radiation, latent heat, sensible heat, and heat exchange through sea ice. It is found that the total heat flux anomaly in the model is dominated by anomalies in the sensible heat flux and the latent heat flux. Figure 11 shows the time series of these particular anomalies averaged over an area between 46 and 65 S and between 190 and 220 E. This area has been chosen because it includes one of the three local centers of maximum heat flux anomalies in the wavenumber-3 pattern of the heat flux field (see section 4b). For clarity only the first 20 yr (year 200 year 220) of data are shown. It is seen that when latent and sensible heat loss is anomalously low, the net heat flux into the ocean is anomalously high, and vice versa. Further, the latent and sensible heat flux anomalies have similar magnitudes. b. Dominance of wavenumber-3 pattern in the oceanic fields In section 3, we have demonstrated the predominance of a wavenumber-3 pattern in the atmospheric variability on interseasonal and interannual timescales. A base point identical to that identified by the observational study of MW was used in the cross-correlation analysis. In this section we focus on the interannual oceanic variability. To avoid having to choose a base point, we apply EOF analysis to filtered oceanic anomalies. The pattern of the first EOF for the filtered zonal current anomalies is shown in Fig. 12 and the temporal coefficient in Fig. 13. This first mode accounts for 17.4% of the total variance. Again a predominant wavenumber-3 pattern appears (Fig. 12) and the dominant timescale is 4 5 yr. In order to facilitate a comparison between the oceanic and atmospheric variability (see section 5), the MSLP I 3ann is also plotted in Fig. 13. The two curves share similar fluctuations and the correlation coefficient is 0.81. The second EOF explains only 6.8% of the total variance and shows a zonal symmetric pattern. A similar EOF analysis has been performed on

3094 JOURNAL OF CLIMATE VOLUME 12 SST, mixed layer depth, and the surface heat flux. All of the three first-mode patterns indicate that wavenumber 3 is dominant (figure not shown). Wavenumber 3 is also the preferred pattern in the ACW described by CBR. In order to examine the coherence of the variabilities of heat flux, surface temperature, and convective mixed layer depth, a combined EOF analysis of these fields has been undertaken. The first mode accounts for 19.3% of the total variance. The patterns of the first EOF mode are shown in Fig. 14, and the corresponding temporal coefficients, in Fig. 15. (As mentioned above, the area between 46 and 65 S and between 190 and 220 E includes a center of maximum heat flux anomalies.) It is seen that when an anomalous oceanic heat loss takes place, surface cooling ensues and the convective mixed layer deepens, and vice versa. Also plotted in Fig. 15 is the temporal coefficient of the first EOF mode for the anomalies of surface oceanic zonal current (Fig. 13). Comparing the two curves, it is seen that they share similar fluctuations. The correlation coefficient is 0.78. The second mode explains only 7.3% of the total variance and again shows a zonally symmetric pattern. To describe temporal phase relations between these different oceanic interannual variations, time-lagged cross correlations have been carried out. Time series of filtered anomalies in heat flux, SST, level-5 temperature (T5), and mixed layer depth are constructed by averaging over the area between 46 and 65 S and between 190 and 220 E. Shown in Fig. 16 is the correlation between heat flux and SST, heat flux and T5, and heat flux and mixed layer thickness. The heat flux and SST are almost 90 out of phase, and a heat gain by the ocean is followed by warm SSTs about 10 months later. A similar relation exists between heat flux and T5. Thus when warming takes place, it occurs throughout much of the water column. On the contrary, heat flux is almost 180 out of phase with mixed layer depth; that is, anomalous heat loss precedes an anomalous mixed layer deepening by about 5 months. A similar analysis has been performed at the other centers of maximum heat anomalies, and similar results were obtained. The feature that heating or cooling takes place throughout much of the water column is interesting. It indicates that the oceanic thermohaline anomalies are forced by surface heat flux anomalies and are not the result of internal oceanic processes. In the latter case, vertical heat redistribution would mean that a surface cooling must be associated with a subsurface warming. FIG. 7. Composites of meridional wind stress anomalies (N m 2 ) for (a) strongest positive and (b) strongest negative values of the index I 3ann for MSLP. (c) The difference between (a) and (b). Contour interval is 0.02 for (a) and (b) and 0.03 for (c). Positive values are shaded.

OCTOBER 1999 CAI ET AL. 3095 FIG. 8. Hovmoeller diagrams of anomalies, averaged meridionally between 46 and 65 S; (a) temperature ( C) and (b) salinity (ppt) anomalies at level 5 (215 m). Contour interval for (a) is 0.05 and for (b) is 0.003. Positive values are shaded. 5. Coupling of interannual atmospheric and oceanic variability a. Dynamics of the modeled ACW and its self-sustaining process An inspection of Figs. 6, 13, and 15 reveals that the wavenumber-3 indices and the temporal coefficients of the leading EOF modes are strikingly similar. Correlation coefficients between these curves range from 0.78 to 0.86. Thus these indexes appear to be different aspects of a single ocean atmosphere pattern of variability. These persistent variations, which have periods of 4 5 yr and portray anomalies from the middle atmosphere right down to the middepths of the ocean, are hereafter referred to as the modeled ACW. In section 3, it was shown that the variability associated with I 3 is separate from any model ENSO-like fluctuations. Therefore the modeled ACW is not associated with modeled ENSOlike variability. As described in previous sections, in the present study

3096 JOURNAL OF CLIMATE VOLUME 12 FIG. 9. As for Fig. 8 but for (a) temperature anomalies ( C at level 8) and (b) volume mean temperature anomalies. Contour interval is 0.05 for (a) and 0.02 for (b). the MSLP anomalies are characterized by standing oscillations with preferred fixed locations (centers) for strongest anomalies. Time longitude plots (constructed in the same way as for Figs. 8 and 9) of anomalies in air sea heat flux and mixed layer thickness also show standing oscillations rather than eastward propagation (figures not shown). To determine the temporal spatial evolution of thermohaline anomalies in relation to the MSLP standing oscillations, a correlation analysis using the time series of MSLP I 3ann (Fig. 6) as a reference time is carried out [see Zhang and Levitus (1997) for a similar analysis on an ENSO event using the temporal coefficient of a leading EOF]. Temporal correlation coefficients are computed between this time series and the SST anomalies at individual grid points for different time lags (Fig. 17). The figure has three panels that show time lags from year 0 with a 1.25-yr interval (approximately one quarter-cycle), representing the space time evolution for a half-cycle. A positive lag means that the I 3ann leads the gridpoint data. For a given lag, a positive correlation coefficient at a location implies that the variation of the SST anomaly at this location is in phase

OCTOBER 1999 CAI ET AL. 3097 FIG. 10. As for Fig. 8 but for density anomalies (kg m 3 ) at level 5; (a) total density anomalies, (b) anomalies induced by temperature, and (c) anomalies induced by salinity. Contour interval is 0.005. with the MSLP fluctuation at the three centers but lags by the given time. As the MSLP oscillates, SST anomalies move eastward. During the half cycle, a positive SST anomaly propagates for about one-sixth of the route around the pole. Using the I 3 index for Z or the temporal coefficient from the combined EOF (Fig. 15) as a reference time yields similar results. Figure 17 demonstrates the underlying dynamics of the modeled ACW. A schematic diagram of this is presented in Fig. 18, which reflects the interrelationship among modeled ACW components as well as the feature of an equivalent barotropic response of the mid- to highlatitude atmosphere to underlying SST anomalies. In this discussion, it is assumed that the atmospheric variability is stationary (for reasons still unknown) and that the upper ocean anomalies are advected eastward by the ACC. Let a starting point be taken as the time (year 0) with a setting similar to that displayed in Fig. 18a. At this time, there is an anomalously high MSLP south of Western Australia, an anomalous low south of New Zea-

3098 JOURNAL OF CLIMATE VOLUME 12 FIG. 11. Time series of anomalies (W m 2 ) in total heat flux into the ocean, and latent heat flux and sensible heat flux averaged from the ocean to the atmosphere, over an area between 46 and 65 S and between 190 and 220 E. land, and an anomalous high in the central Pacific. As a result of geostrophic balance, an anomalous positive (northward) meridional wind stress is generated (Fig. 7a) in the region between the south of Western Australia and the south of New Zealand, bringing cool and dry subpolar air to the region. This cools the ocean, not only at the surface but, by oceanic convection, through much of the oceanic water column (Figs. 8 and 14). The cooling takes place in the form of anomalously stronger latent and sensible heat loss to the atmosphere, both with a comparable magnitude (Fig. 11). Consequently, a negative SST anomaly is generated (Fig. 17). In an FIG. 12. Pattern of the first EOF mode of anomalies in the zonal surface ocean current. identical way, but in a reverse sense, a positive SST anomaly is produced in the region between the south of New Zealand and the central Pacific; here a negative (southward) meridional wind stress anomaly brings warm lower-latitude air to the region, leading to an anomalously weaker latent and sensible heat loss to the atmosphere. Once generated, these SST anomalies are advected eastward by the surface ACC. As the negative SST anomaly approaches the center of the anomalously low MSLP in the south of New Zealand, the air above becomes anomalously cool leading to an increasing MSLP in the center. During the course of its eastward movement the SST anomaly is weakened (see Fig. 17b; this is indicated by the smaller number of contours in Fig. 18b). By the end of the first quarter-cycle, a weakened negative SST anomaly is at a location near the center of the originally negative MSLP anomaly in the south of New Zealand, and the MSLP anomaly is at a midpoint ascending from below normal to above normal as implied by the correlation map of Fig. 17b (in Fig. 18b, the MSLP anomalies are not indicated because they are at midpoints). In a similar way, the MSLP anomaly in the central Pacific, being approached by the positive SST anomaly from its western side, is at a midpoint

OCTOBER 1999 CAI ET AL. 3099 FIG. 13. Temporal coefficient of the pattern shown in Fig. 12 (solid line). Superimposed is the I 3ann for MSLP (dashed line) shown in Fig. 5. descending from above normal to below normal. During the second quarter of the cycle these SST anomalies continue to be advected eastward, and the MSLP anomalies at all centers reverse their signs. In the region between the south of New Zealand and the central Pacific, the meridional wind stress anomaly changes from negative (southward) to positive (northward) bringing cool and dry air to the region, cooling the ocean, and reinforcing the negative SST anomaly entering the region from the west. By the end of the half-cycle (Fig. 18c), a situation opposite to that of year 0 is established, ready to proceed with the reverse phase of the cycle. Thereafter, the self-sustaining process repeats itself. Two features emerge. First, it is the speed of the surface ACC that determines the frequency of the sign change of the MSLP anomalies at their centers. Second, any SST anomaly needs to persist for only half of the local MSLP oscillation cycle; it is reinforced at a time interval of half a local MSLP oscillation cycle. During the time of a half-cycle, the anomaly propagates onesixth of the route around the pole, that is, 60 longitude. The overall wavenumber-3 pattern means that it would take a time of three standing oscillation cycles for the surface ACC to advect an anomaly around the pole. During this circuit, the SST anomalies are reinforced six times over. It is the continuing reinforcement of anomalies at fixed locations that gives the impression of continuous eastward movements around the globe. In our study, the mean surface current is 5.2 cm s 1 (Table 1); therefore the anomalies would take 12 16 yr to encircle the pole. The essential elements of the dynamics are the underlying surface ACC advecting the SST anomalies and the interaction between SST and MSLP anomalies. On the one hand, the generation of SST anomalies relies on MSLP anomalies and its associated meridional wind stress anomalies (via geostrophic balance) to produce air sea flux anomalies. On the other hand, the MSLP anomalies can only change sign when negative (positive) SST anomalies are brought to locations near centers of negative (positive) MSLP anomalies. This is facilitated by the surface ACC. Considering the hypothetical case of an absence of the ACC together with fixed locations for strongest MSLP anomalies, the setting of positive (negative) SST anomalies lying to the western side of positive (negative) MSLP anomalies (Fig. 18a) would remain in these locations, and there would be no oscillations in the MSLP and, hence, no ACW. Some reconciliation between the observed and modeled ACW is needed. One aspect is the observed phase locking of ACW components, all moving eastward. This feature has not been produced by coupled models. The other aspect is the zonal wavenumber 2 in the observed ACW (WP; Jacobs and Mitchell 1996) as opposed to the wavenumber-3 pattern both observed (e.g., MW) and

3100 JOURNAL OF CLIMATE VOLUME 12 modeled by the present study and by the CBR study. CBR suggested that when a longer observational record on ACW is available, the wavenumber-3 pattern may appear. They reported that, at some decades, there sometimes exists a dominant wavenumber-2 pattern in their model. In our model it seems that wavenumber 3 is the preferred pattern, and there is no evidence of a wavenumber-2 pattern in the 60-yr period. In a companion study, Baines and Cai (1999) describe an interactive instability mechanism in an idealized atmosphere ocean interactive model. The interaction occurs between long barotropic Rossby waves in the atmosphere forced by vortex stretching caused by surface heat flux from the ocean and similarly long waves in the upper layer of the ocean forced by wind stress curl. A normal-mode analysis of the inviscid form of the system shows it to be unstable for zonal wavenumbers 1 3 (although growth rates are small), and the phase relationships between the atmosphere and ocean are similar to those at the half-cycle points (as shown in Fig. 18). If the instability analysis is carried out with the large viscosity and diffusivity coefficients as used in our coupled model, all disturbances are damped, but the damping is weak and increases with zonal wavenumber. FIG. 14. Patterns of the first mode of the combined EOF for (a) heat flux anomalies, (b) SST anomalies, and (c) anomalies in mixed layer thickness. The anomaly of each field is normalized by the standard deviation at each grid point before applying the EOF analysis. b. Other associated oceanic dynamic processes Figure 12 shows a clear wavenumber-3 pattern in the surface zonal current. This is caused by meridional wind stress anomalies. As discussed above, when there is a positive MSLP anomaly in southwest Australia and a negative MSLP anomaly in the south of New Zealand, in between there is a positive (northward) meridional wind stress anomaly. When this anomalous stress acts on the surface of the ocean, an anomaly in zonal flow is generated due to the Coriolis effect, which is directed from the center of negative MSLP anomalies toward the center of positive MSLP anomalies. Figure 10 shows that there are also strong saline ACW signals. The generation of these signals is now described. In southern mid- to high latitudes, the model ocean is characterized by convective activity. The vertical stratification is either very weak or nonexistent. Due to the global hydrological cycle, there is a net freshwater input into the oceans at these latitudes. As a result surface freshening occurs, which leads to a salinity minimum at the surface (Table 1). Thus the vertical structure of salinity is such that at the subsurface the salinity is greater than that at the surface. Associated with this feature and the weak stratification, the surface water is cooler than the subsurface water. The vertical structure of temperature (Table 1) shows a maximum at the subsurface. This structure means that when vertical mixing is enhanced, warmer and more saline subsurface water is brought to the surface. In the ACW anomalies, heating or cooling takes place throughout much of the water column, and the subsurface temperature variability lags that of the surface temperature by about 4 months (Fig. 16). This lag is largely caused by the change in the convective mixed layer thickness. Suppose that the ocean is being heated. As heating proceeds, the convective mixing weakens, and less heat is transported from the warmer subsurface water to the surface. This offsets the surface warming and at the same time reinforces the subsurface warming. Thus the subsurface warming is able to continue after the surface warming reaches a maximum, leading to the lag. Similarly, reduced convective mixing leads to increased salinity at the subsurface because less salt is transmitted to the surface. This gives rise to the feature that positive SST anomalies are associated with subsurface positive salinity anomalies, and vice versa (Fig. 10). 6. Discussion and summary Variability of the southern atmospheric circulation at mid- to high latitudes with a dominant quasi-standing wavenumber-3 oscillating pattern has been reported in many observational studies. The signals are barotropic

OCTOBER 1999 CAI ET AL. 3101 FIG. 15. Temporal coefficient (normalized by a factor of 500) of the patterns shown in Fig. 14 (solid line). Superimposed is the temporal coefficient of the first-mode EOF for zonal surface ocean current (dashed line) shown in Fig. 13. FIG. 16. Time-lagged cross correlations between the time series of (60 yr) anomalies in heat flux and SST, heat flux and level-5 temperature (T5), and heat flux and mixed layer (ML) depth. The time series of these anomalies are constructed by averaging over an area between 46 and 65 S and between 190 and 220 E. and are seen in the middle atmosphere as well as at the atmosphere ocean interface. Moreover, there are preferred fixed centers for the strongest anomalies (e.g., MW). These features are well reproduced by the CSIRO coupled model on various timescales. There are several shortcomings that are to be expected from such a comparatively low-resolution coupled model (R21) used here. It is likely that low resolution may be accountable for some difference between the modeled and observed ACW features. In particular, ENSO events are not realistically simulated; this may be the reason that an observed teleconnection between southern mid- to high-latitude variabilities and ENSO is not reproduced by the model. An index of the wavenumber-3 pattern in the mid- to high latitudes in our model is not correlated with the model Southern Oscillation index, suggesting that the variability of the former is separate from the modeled ENSO-like events. However, the wavenumber-3 index is strikingly similar to and highly correlated with various aspects of oceanic variability on a 4 5-yr timescale; these oceanic anomalies move eastward and are similar to the observed ACW as reported by WP. In our model it is found that these anomalies exist not only at the surface but through to the middepths of the ocean, with a similar barotropic nature to that of the atmospheric anomalies and with a dominant wavenumber-3 pattern (instead of wavenum-

3102 JOURNAL OF CLIMATE VOLUME 12 FIG. 17. Distributions of the correlation coefficients between the time variations of the I 3ann for sea level pressure and SST with the time variation leading the gridpoint data by (a) 0, (b) 1.25, and (c) 2.5 yr. Positive values are shaded and contour interval is 0.2. FIG. 18. Schematic representation of the dynamics of the modeled ACW. The preferred centers for strongest MSLP anomalies are indicated by H and L representing positive and negative MSLP anomalies at a given instant. These centers are associated with the wavenumber-3 pattern. The MSLP anomalies change sign as positive (negative) SST anomalies (solid contours for positive anomalies) are advected by the surface ACC to a location below the centers of positive (negative) MSLP anomalies. The positive (negative) SST anomalies are generated by southward (northward) meridional wind stress anomalies, which are, in turn, induced by geostrophic balance. The southward (northward) meridional wind stress anomalies (indicated by big arrows) bring warm (cool) air to a region between two centers (one H and one L); this leads to an anomalously weak (strong) latent and sensible heat loss to the atmosphere, warming (cooling) the ocean. Shown in (a) is a spatial relationship when the MSLP anomaly south of Western Australia is at a maximum, and in (b) and (c) one quartercycle and half-cycle later, respectively. In (b), a MSLP anomaly is at a midpoint about to change sign and the signs of MSLP anomalies are not indicated. ber 2 in the observed). This overall wavenumber-3 pattern is referred to as the modeled ACW. The essential elements of the dynamics of the modeled ACW are the interactions between SST and MSLP anomalies and the advection of SST anomalies by the surface ACC. Associated with the standing wavenumber-3 pattern, there are fixed centers for strongest MSLP anomalies. These MSLP anomalies change sign as positive (negative) SST anomalies advected by the surface ACC approach the centers of positive (negative) MSLP anomalies. This relationship between SST and MSLP anomalies is revealed by time-lagged cross correlations between them. It suggests that the southern mid- to high-latitude atmosphere has an equivalent barotropic response to underlying SST anomalies. While a linear response can be baroclinic (Hoskins and Karoly 1981), a nonlinear barotropic response may arise from anomalous heat fluxes (Palmer and Sun 1985; Peng et al. 1995; Latif and Barnett 1994, 1996). This barotropic response is confirmed by the analytical study of Baines and Cai (1999). The positive (negative) SST anomalies in our model were generated as follows. The MSLP anomalies produce southward (northward) meridional wind stress anomalies as a result of geostrophic balance. The southward (northward) meridional wind stress anomalies bring warm (cool) air to a region between two centers, one positive and one negative. This leads to to an anomalously weak (strong) latent and sensible heat loss to the atmosphere, which warms (cools) the ocean. Once the MSLP anomalies change sign, warm (cool) SST anomalies are generated again, reinforcing the anomalies entering from the west. The time for the surface ACC to advect one-sixth of the route around the pole determines the frequency of the standing MSLP oscil-

OCTOBER 1999 CAI ET AL. 3103 TABLE 1. Vertical profile of modeled salinity (S), temperature (T), and zonal velocity. These profiles are obtained by averaging over 10 yr and over the latitude band from 46 to 65 S. Level 1 2 3 4 5 6 7 8 9 10 11 12 Depth of (T, S) (m) 12.5 37.5 70.0 125.0 215.0 370.0 635.0 1025.0 1575.0 2350.0 3250.0 4150.0 Salinity (ppt) 34.04234 34.05090 34.12471 34.26925 34.27090 34.27165 34.27166 34.27176 34.27404 34.28598 34.29576 34.31928 Temperature ( C) 0.07092 0.09756 0.47553 1.28336 1.26119 1.24019 1.17769 1.10152 0.93365 0.61605 0.63161 0.81098 Zonal velocity (cm s 1 ) 5.19801 4.70631 4.68548 4.56876 4.41609 4.15475 3.69234 2.99252 2.04536 0.93392 0.18064 1.49395 lation. In every standing oscillation cycle, MSLP anomalies at a center change sign twice. In our model the speed of the surface ACC is such that the period of the standing oscillation is 4 5 yr, implying a timescale of 12 15 yr for an anomaly to encircle the pole. This variability in the model is coherent from the middle atmosphere to the ocean interior. The ACW signals in the ocean interior are not surprising, given the presence of widespread convection in the region. Regardless of whether there is anomalous cooling or heating at the surface, the associated changes in convective mixing mean that there will always be significant changes to the interior of the ocean. For example, when an anomalous atmospheric warming takes place, it leads to a rise in SST. Almost simultaneously, convective mixing is weakened and less of the warmer subsurface water is transported to the surface. This in turn offsets the initial surface warming. At the subsurface, there is an anomalous warming because less heat is transported to the surface. This process has also been found to operate in this coupled model when forced by increased atmospheric CO 2 concentration, producing a maximum subsurface warming in the region (Cai and Gordon 1998). It is interesting to note that the speed of eastward movement of temperature anomalies appears to be independent of depth (Figs. 8a and 9a). As can been seen from Table 1, the zonal velocity decreases with depth. This suggests that, below the surface, advection does not play a significant role in the eastward movement of the anomalies. It seems to support the notion proposed by CBR that the speed of SST anomalies is linked to the wavenumber and frequency characteristics of the forcing and that the speed of the ACC is not important and only influences the magnitude of the anomalies. However, CBR did not address the the cause of the local frequency [4 5 yr; see Weisse et al. (1997) for a discussion of this point]. In the present study it has been demonstrated that the speed of the surface ACC determines the frequency characteristics of the forcing (section 5a). In our model it takes 2 2.5 yr (half of the period of the local cycle) for the model surface ACC to advect an SST anomaly from one fixed center of an MSLP anomaly to another. It is the eastward movement of an SST anomaly that changes the sign of an MSLP anomaly, enabling the MSLP to oscillate. Without the surface ACC, there would be no oscillation in MSLP and hence no ACW. Therefore, a surface ACC is an indispensable component of the modeled ACW. Only below the surface does the ACC not play a significant role in determining the seemingly continuous eastward movement; the ACC still advects temperature anomalies eastward, but because of the weaker speed (compared to the surface ACC), during a half-cycle of the standing oscillation a temperature anomaly is advected for a distance less than 60 longitude. However, in every 60 longitude space interval an anomaly of the same sign is generated. It is the connection of these locally (and vertically) induced anomalies that gives the impression of continuous eastward movements around the globe and an apparent independence of the eastward speed of the current. In summary, since the description of the observed ACW by WP and Jacobs and Mitchell (1996), several studies have reported ACW-like variability in coupled ocean atmosphere models. However, the dynamics remains unclear. The present study describes the interaction among various ACW components and presents a plausible mechanism for a self-sustaining process in which an ACW can maintain itself against dissipation as it travels around the globe. Acknowledgments. This work was supported by a core greenhouse grant from the Australian Department of Environment, Sport and Territories. The assistance of members of the Climate Modelling Program and the Atmospheric Processes Program at the CSIRO Division of Atmospheric Research in developing parts of the coupled model used in this study is gratefully acknowledged. WJC benefited from discussion with J. S. Frederiksen, I. N. Smith, P. H. Whetton, and S. G. Wilson. The EOF analysis package was adapted from that developed by S. G. Wilson. Some of the figures were generated by means of graphics interface packages provided by Harvey Davies. REFERENCES Baines, P. G., and W. J. Cai, 1999: Analysis of an interactive instability mechanism for the Antarctic circumpolar wave. J. Climate, in press. Bryan, K., 1969: A numerical method for the study of the circulation of the world ocean. J. Comput. Phys., 4, 347 376. Cai, W. J., and H. B. Gordon, 1998: Responses of the CSIRO climate model to two different rates of CO 2 increase. Climate Dyn., 14, 503 516., and, 1999: Southern high-latitude ocean climate drift in a coupled model. J. Climate, 12, 132 146. Christoph, M., T. P. Barnett, and E. Roeckner, 1998: The Antarctic