Journal of Marine Systems 82 (2010) 304–310

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Journal of Marine Systems

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Variability of the Southern Ocean fronts at the Greenwich Meridian

W. Billany ⁎, S. Swart, J. Hermes, C.J.C. ReasonDepartment of Oceanography, University of Cape Town, Rondebosch, 7701, South Africa

⁎ Corresponding author.E-mail address: Chris.Reason@uct.ac.za (W. Billany)

0924-7963/$ – see front matter © 2010 Elsevier B.V. Adoi:10.1016/j.jmarsys.2010.06.005

a b s t r a c t

a r t i c l e i n f o

Article history:Received 8 October 2009Received in revised form 2 June 2010Accepted 10 June 2010Available online 17 June 2010

Keywords:FrontsSouthern OceanVariability

Maps of Absolute Dynamic Topography (MADT) at the Greenwich Meridian are used to identify the locationsand gradients of the various fronts in the Southern Ocean (Subtropical Front, Sub-Antarctic Front, AntarcticPolar Front, Southern ACC Front, and Southern Boundary of the ACC). It is found that the frontal gradients inSea Surface Height (SSH) used to determine these fronts are consistent with those determined fromhydrographic data. A strong relationship was found to exist between the position of all the fronts and thegradients of SSH except for the Southern Boundary (SBdy) front. Substantial seasonality and interannualvariability in the frontal positions is found. All the fronts except the Antarctic Polar Front (APF) show apoleward tendency in position over the last 15-years.Using the MADT-derived frontal positions, the meridional zones between the fronts are examined and themean zonal sea surface temperature (SST) is used to consider the surface variability in these frontal zones. Inaddition to the strong seasonality and interannual variability in the SST of these frontal zones, there is also atendency towards warming (cooling) of the Sub-Antarctic and Antarctic Polar Zones (Southern BoundaryZone). The tendencies in the frontal positions are consistent with a warming in the Southern Ocean exceptnear the APF and the Southern ACC Front (SACCF).

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1. Introduction

The mid to high latitudes of the Southern Hemisphere arecharacterised by persistent eastward flow in both the ocean (mainlythe Antarctic Circumpolar Current—ACC) and the atmosphere as wellas a number of pronounced ocean fronts that orientate themselvesmore or less zonally around the hemisphere. The ACC is an importantcomponent of the climate system (Gordon, 1986; Rintoul, 1991;Sloyan and Rintoul, 2001a,b; Speich et al., 2001) and, thus, a betterunderstanding of the fronts associated with it, is needed. However,the remoteness of the Southern Ocean and its harsh environmentalconditions means that this region is poorly sampled by hydrographicsections.

On the other hand, the continuity and coverage of satellite altimetrydata allows the Southern Ocean and the ACC to be studied in greaterdetail than before. The use of Maps of Absolute Dynamic Topography(MADT) to locate fronts in the ACC has in previous studies (Sokolov andRintoul, 2007; Swart et al., 2008; Sokolov and Rintoul, 2009a,b; Swartet al., 2010) has proven to be consistent with those fronts inferred fromhydrographic data (Orsi et al., 1995). Furthermore, specific contours ofSea Surface Height (SSH) are associated for the most part withindividual fronts of the ACC along their circumpolar paths in bothtime and space (Sokolov and Rintoul, 2007; Swart et al., 2008; Sokolov

and Rintoul, 2009a,b; Swart et al., 2010). Thus, the application of MADTis extremely useful in a challenging location like the Southern Ocean.

The Southern Ocean is a region with a clear secular warming signal(Gille, 2002, 2008) and one also influenced by the Southern AnnularMode (SAM). The SAM is the dominant mode of atmosphericvariability in the extra-tropical Southern Hemisphere (Thompsonand Wallace, 2000; Hall and Visbeck, 2002; Thompson and Solomon,2002; Marshall, 2003). Hall and Visbeck (2002) suggested that thereshould be a degree of co-variability between the SAM and theSouthern Ocean. In the circumpolar ocean region, westerly windsprevail, driving a northward Ekman drift within the ACC. As warmwaters are kept away from the high latitudes in the SouthernHemisphere, the density gradient is further enhanced. The resultingdensity gradient augments the eastward flow of the ACC. Further-more, the westerly winds track across different latitudes over theSouthern Ocean and, in doing so, waters within the Ekman layer movenorth and south depending on the latitudinal location of the westerlywinds and the associated wind stress curl. The southward andnorthward moving waters converge and diverge, creating frontalregions and divergent zones within the ACC.

Given this impact of the SAM on surface winds and associatedupper ocean zones of convergence/divergence, it is possible that thelocations and gradients of the Southern Ocean fronts will also beinfluenced by the SAM. Models suggest that it is likely that the SAM isresponsible for at least part of the variability in the ACC (Hall andVisbeck, 2002; Sen Gupta and England, 2006). Sallée et al. (2008)went one step further and usedMADT to locate the twomain fronts of

Fig. 1. The mean of the 15-year (1993–2007) MADT data (solid line) and SSH gradient(dashed line) from a meridional transect along the Greenwich Meridian. Labelled arethe mean frontal positions of the ACC at the Greenwich Meridian found using MADTdata and peaks in SSH gradient.

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the ACC, the Sub-Antarctic Front (SAF) and Antarctic Polar Front (APF)and investigated if these fronts respond to changes in the phase of theSAM over a 13-year period. These authors suggested that the SAF andAPF in the Atlantic region shift southward during a positive SAM.However, they did not describe the fronts and their variability in theAtlantic sector west of 40°E in detail. Nor did they investigate theother fronts within the ACC as is done below. Sallée et al. (2008) alsoemphasised that while the SAM is highly zonal in its structure, thefrontal response is not, and exhibits strong regional differences. Thissituation is in part due to the topographic constraints at any givenlongitude that influences not only a front's position, but also itsintensity. A study by Burls and Reason (2006) found that the SSTgradient of the Subtropical Front (STF) in the central South Atlanticexhibited pronounced interannual and mesoscale variability over theobserved period 2002–2005 and that the strength and location of theSTF SST-derived frontal gradient was related to changes in the windstress field.

In this study, MADT is used to locate the Southern Ocean frontalpositions at the Greenwich Meridian and to examine their associatedvariability. We validate whether the frontal positions derived fromaltimetry data agreewith the frontalpositions foundbyOrsi et al. (1995)from hydrographic data.We then aim to describe the surface variabilityof the zones between the fronts of the ACC, at the GreenwichMeridian,using Microwave Optimally Interpolated (MOI) SST data. Finally, weinvestigate whether the dominant modes of climate variability in theSouthern Hemisphere, the SAM and the El Niño Southern Oscillation(ENSO) play a role in the interannual variability of theACCand its fronts.

The Greenwich Meridian was chosen as the study location as it isreasonably isolated from the downstream perturbations caused by theDrake Passage and is far enough to the west to not be stronglyinfluenced by the flow of the Agulhas Current and its retroflection.Also, the GoodHope cruise track, which was established in early 2004,follows the Greenwich Meridian southward of approximately 50°S(http://www.ifremer.fr/lpo/speich/GOODHOPE/goodhope.htm,Ansorge et al., 2004; Speich and Arhan, 2007; Swart et al., 2008;Gladyshev et al., 2008). The availability of such in-situ hydrographicdata is useful when testing the ability of altimetry data to track theACC fronts.

2. Data

The MADT are the sum of the sea level anomaly data and meandynamic topography from Rio and Hernandez (2004). The meandynamic topography is obtained through a combination of in-situmeasurements (hydrographic and surface drifter data), altimetry dataand the EIGEN-GRACE 03S geoid. The altimetry products are producedfrom the combination of four different altimeter instruments onindividual satellite platforms: Topex/Poseidon, Jason-1, ERS-1/2 andENVISAT. The altimetry data are available at weekly time-steps with aspatial resolution of 1/3° and mapped on a Mercator grid. The MADTdata are referenced to a seven-year (1993–1999) mean. For furtherinformation on mapping techniques and error corrections applied tothese fields, refer to Le Traon et al. (1998), Le Traon and Ogor (1998)and Ducet et al. (2000).

To help assess variability in the frontal zones, blended MOI SST data(available from June 2002) are used. The SST data are blended using theoptimal interpolation method of Reynolds and Smith (1994). The MOISST product has a spatial resolution of 0.25° (∼25 km) and is availabledaily. Unfortunately the MOI SST product relies on only the AdvancedMicrowave Scanning Radiometer (AMSR-E) instrument at latitudesgreater than 40° from the equator. Furthermore, the mean spatialresolution of the AMSR-E instrument is coarser at ∼58 km and it doesnot have daily global coverage. Nonetheless, the swath gaps andreduced spatial resolution do not pose a serious issue when consideringdata consolidated inmonthly averages, as is the case in this study. Near-real-time validation and bias corrections are undertaken using in-situ

data retrieved from the Global Ocean Data Assimilation Experiment(GODAE).

The SAM index used here is based on station sea level pressure dataas described in Marshall (2003). This index is preferred over a purelyreanalysis based index as biases in reanalysis constructions in the SAMhave been found (Hines et al., 2000). In addition, theMultivariate ENSOIndex (MEI) (Wolter and Timlin, 1998) is used to consider possiblerelationships with ENSO.

3. Frontal characteristics and associated variability inferred fromsatellite altimetry

Tracking meridional positions of the ACC fronts at the GreenwichMeridian using an isoline of MADT that consistently coincides withthe largest long-term mean gradient in the SSH (Fig. 1) associatedwith a particular front proved reasonably robust. This method alsoagreed with the hydrographic derived frontal positions as defined byOrsi et al. (1995) at the Greenwich Meridian (Table 1).

Fig. 2 displays the MADT-derived frontal positions at the Green-wich Meridian plotted over the altimeter derived geostrophic surfacecurrent speed for each month for January 1993–December 2007. It iswell known that the fronts of the ACC consist of multiple branchesthat merge and diverge along their circumpolar paths (Sokolov andRintoul, 2007). However, for simplification and consistency, we onlylocate the main branch of each front of the ACC at the GreenwichMeridian and define the frontal zones by thesemain branches. Using asimilar method, Swart and Speich (2010) found that the isolines ofMADT closely follow the surface velocity magnitudes of the main ACCfronts along the GoodHope transect.

To validate the frontal positions found using isolines of MADTderived from satellite altimetry (Table 1), we found the difference inlatitude between the frontal position using a single isoline of MADTand themaximum gradient foundwithin a zonal band associated witheach front. In Fig. 3, the difference in degrees of latitude between thevarious fronts and their associatedmaximum gradients is representedin a histogram to demonstrate howwell the frontal locating techniquehas worked. The majority of the differences are b0.6° latitude(b60 km). For cases that are equal or less than 0.3° in difference inlatitude, the range is 51−77%, with the least accurate being the STFand the APF being the most accurate. For errors up to 0.6° in latitude(i.e., up to about 60 km), the frontal positions meet this criterionbetween 68% (STF) and 97% (Southern ACC Front—SACCF) of the time.The STF frontal position determined in this way is the least accuratemainly due to mesoscale features such as Agulhas Rings that track

Table 1Criteria used to locate the ACC Fronts, reproduced from Orsi et al. (1995).

Front Criteria Position (°S) defined byOrsi et al. (1995)

MADT-derived meanfrontal position (°S)

Frontal position standarddeviation (°)

MADT values followed(dyn m)

STF 10 °Cbθ100mb12 °C 38.4 38.5 0.56 1.56SAF Sb34.20 at Zb300 m

θN4–5 °C at 400 m45.7 45.3 0.31 1.90

APF θb2 °C along θmin at Zb200 m 49.4 50.0 0.24 0.58SACCF θN0 °C along θmin at Zb150 m 52.4 53.5 0.19 0.19SBdy Southern limit of vertical maximum of θN1.5 °C, (∼200 m) 56.1 55.6 0.28 −0.06

The fronts in the table are as follows; Subtropical Front (STF), Sub-Antarctic Front (SAF), Antarctic Polar Front (APF), Southern ACC Front (SACCF), Southern Boundary of the ACC(SBdy). θ is the potential temperature, S is the salinity. The positions determined by Orsi et al. (1995) are for the Greenwich Meridian. The MADT-derived mean frontal position andassociated standard deviations are given for each front.

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across the Greenwich Meridian in the region of the STF. Agulhas Ringspropagate westward after being shed from the Agulhas Retroflectionregion and can ‘mask’ the true frontal position of the STF due to theirhigh SSH gradients. Table 1 indicates that the STF is the most variableof the ACC fronts at the Greenwich Meridian (standard deviation offrontal position is 0.56°) whereas the SACCF is the least variable in itsposition (standard deviation 0.19°).

Figs. 1 and 2b–c indicate that both the SAF and the APF areparticularlywell definedwhen usingMADT. Themain branch of the SAFis chosen in this study. However, it is apparent that there is oftenanother northern frontal feature of the SAF near 43–44°S (Fig. 2b)consistent with other studies (Moore et al., 1999; Sokolov and Rintoul,2007). The APF is a distinct front and does not appear to have any clearbranches or filaments associated with it at the Greenwich Meridian(Fig. 1). The boundary separating the SACCF and Southern Boundary of

Fig. 2. Collection of Hovmöller plots of the surface geostrophic velocity magnitudes (colourGreenwich Meridian from January 1993 to December 2007. (a) STF, (b) SAF, (c) APF, (d) SA

the ACC (SBdy) is more difficult to distinguish in Fig. 2d–e; however,each of these fronts are distinguished by separate ‘peaks’ in the SSHgradient.

Greater surface geostrophic current speeds tend to be apparent inclose proximity to the STF at the Greenwich Meridian (Fig. 2a) exceptfor four events: September–November 1993, February–March 1998,May 1999–May 2000, and January–March 2007. These cases werefound to be errors in the validation process in that the isoline of theMADT associated with the STF was not being followed. The data wasmanually corrected for these events.

Fig. 4 plots the mean seasonal variations of the ACC fronts at theGreenwich Meridian. It is clear that the STF has the most pronouncedannual cycle (note larger vertical scale in panel (a)), whereas the SAF,and particularly the APF, show smaller seasonal shifts. A rapid changein the STF position occurs from May to August when this front moves

surface plot; in ms−1) and latitudinal frontal positions of the ACC (black lines) at theCCF, (e) SBdy.

Fig. 3. The frequency (in %) of the categorised distances (in degrees latitude) betweenthe MADT-deduced ACC front positions and the position of the local maximum SSHgradient associated with that particular front, at the Greenwich Meridian.

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from its southernmost (38.8°S) to its northernmost position (38.3°S).Fig. 4e indicates that the more poleward of the two southernmostfronts, the SBdy, leads the SACCF by approximately one month, andthat the magnitude of the seasonal variability of the SBdy is greaterthan that of the SACCF. Inherent and expected deviations from themean seasonal cycle are present throughout Fig. 4 due to the limitedtime-series of available altimetry data.

To see whether the characteristics of the seasonality might havechanged, the 15-year time-series was divided into three 5-year blocks.It is known that the phasing of the semi-annual oscillation (SAO) haschanged in recent years (Rouault et al., 2005) and, thus, it is possiblethat this change might have impacted on the fronts. It was found (notshown) that the mean seasonal position of the STF during the 1998–2002 epoch was further north than for the 1993–1997 or the 2003–2007 epochs, with the latter located furthest south. No clear shifts

Fig. 4.Mean seasonal meridional shifts (° latitude) in the frontal positions in the ACC at the Gstandard deviation for each month within the seasonal mean. (a) STF, (b) SAF, (c) APF, (d)

were observed between the epochs in the seasonal positions of theSAF and the APF. For the SACCF and the SBdy, the mean seasonalposition for each successive epoch was located further south for allmonths except March to June (SACCF) and February to April (SBdy).

To better capture the interannual variability in the frontal positionsand gradients, the annual cycle was then removed from the 15-yeartime-series of the frontal positions (Fig. 5). Caution must be exercisedwhen interpreting the results of the STF prior to 2002 since AgulhasRings were found to disturb the MADT contour being used to track theSTF. However after 2002, the SSH gradient and the position of the STFaremuchmore inphase (Fig. 5a). TheSAF, APF, and SACCF showa strongcorrelation between their respective positions and SSH gradientsthrough the 15-year time-series (see Table 2).

There is substantial interannual variability in all of the fronts at theGreenwich Meridian, with identifiable large shifts in the frontalpositions in certain years. Inspection of Fig. 5 suggests that the frequencyof the interannual variability is higher (lower) for the SAF and SBdy(SACCF) fronts. All the fronts except the APF show a poleward tendencyin their frontal positions over the 15-year time-series. Similarsouthward shifts in the ACC fronts have been described by Swart andSpeich (2010) and Sokolov and Rintoul (2009b). The STF, SACCF andSBdy have trends of −0.051, −0.015 and −0.021°latitude year−1,which are highly significant (p≪0.01) respectively. The trend of theAPF is 0.008°latitude year−1 at the 95th percentile and the SAF showedno significant trend. Although the positions of each front on the wholeshow similar tendencies, years of large anomalous shifts in positionappear to differ for each front.

The question arises as to whether the shifts in frontal position ortheir interannual variability might be related to the SAM or to ENSO.The former mode is the most prominent mode in the mid- to highlatitude Southern Hemisphere and is known to have tended to be inpositive phase in recent years (Marshall, 2003) as well as to projectover the southeast Atlantic region (e.g., Reason and Rouault, 2005).On the other hand, ENSO is also known to strongly project into thisregion (e.g., Colberg et al., 2004). To assess possible relationships, thelatitudes of the frontal positions during the 15-year time-series were

reenwich Meridian from a 15-year continuous time-series. The error bars represent theSACCF, (e) SBdy.

Fig. 5. Time-series with themonthlymean seasonal cycles removed from the SSH gradient of the fronts (grey line), meridional frontal positions (black line), with the trend line of themeridional frontal position (dashed line). The trend for each front is given with the significance level (p) greater than the 99 percentile for all fronts with the exception of the APF(N94%) and the SAF that has no significant trend. (a) The STF, (b) the SAF, (c) the APF, (d) the SACCF, (e) the SBdy.

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correlated with the SAM index and with the MEI. However it wasfound that only rather weak correlations (b0.25) existed.

Another way to consider variability in the fronts is to use themonthly mean SST of each frontal zone at the Greenwich Meridian.The frontal positions as defined by the MADT data were used to locatethe boundaries of these frontal zones. The blended MOI SST forthese zones was then normalized using the width of the zone sothat monthly mean zonal SST values for each frontal zone werecomparable. Fig. 6 shows that, in addition to the obvious seasonality,there are also suggestions of possible tendencies in the SST of thefrontal zones. Although the record is short, Fig. 6 suggests a warmingtendency for the Sub-Antarctic Zone (SAZ) and the Antarctic PolarZone (APZ) and a cooling tendency for the Southern Boundary Zone(SBZ). Only the SAZ MOI SST trend was shown to be significant at the95th percentile. The other zonal trends for Fig. 6 are insignificant.

A relatively weak negative correlation (r=−0.22) was foundbetween the mean zonal SST of the SBZ and the SAM i.e. when the

Table 2Correlations between themeridional positions (degrees latitude) and the SSH gradients(dyn m100 km−1) associated with each front of the ACC at the Greenwich Meridian.

Front Correlation coefficient (significance)

STF 0.18 (98%)SAF 0.62 (N99%)APF 0.93 (N99%)SACCF 0.96 (N99%)SBdy −0.60 (N99%)

Correlations were based on the normalised index of both variables with the mesoscaleassociated errors removed.

SAM is strengthened, the SST decreases in the SBZ. A relationshipbetween a positive SAM in recent years and the mean zonal SST of theAPZ (Fig. 6b) is not apparent. However, the APZ mean zonal SST doesshow a warming tendency, which coincides with a slight weakening inthe strength of the positive phase SAM. There is a relatively weakcorrelation (r=0.22) between the SAM and the SACCZ (seasonalityremoved) mean zonal SST.

4. Summary and discussion

In this study we have accurately defined the position of the ACCfronts by trackingvalues ofMADT in space and time. Inmost cases, thesepositions match the local maximum SSH gradients that represent themaximum baroclinic shear in the water column and therefore the coreposition of each front. Additionally, these front positions agree with thefrontal locations described by Orsi et al. (1995). It should be notedhowever that therewere someperiodswhen the position of the STF hadto be manually corrected. These cases occurred when Agulhas Ringscomplicated the altimeter gradients near the STF and highlight some ofthe limitations in the use of MADT as a means of locating and followingfrontal positions in regions of intense mesoscale variability. In general,the STF is known to have a highly variable spatial structure (Belkin andGordon, 1996; Swart et al., 2008; Swart and Speich, 2010) and, asindicated in Fig. 3, is the most difficult front to accurately follow usingthis technique.

The seasonality of the STF, SACCF and SBdymeridional positions areparticularly noticeable. Cooling of the ocean during winter, and theassociated northwardmigration in theMADT isolines are clear. Both theSACCF and the SBdy seasonal shifts share similar characteristics despite

Fig. 6. Plots of the normalized monthly mean zonal SST (with the mean value of each time-series subtracted from each plot) for each zone of the ACC from June 2002–November2007. (a) The SAZ, (b) the APZ, (c) the SACCZ, (d) the SBZ.

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the greater magnitude of the SBdy and its lead of the SACCF by onemonth. The southernmost seasonal position is found in October (SBdy)and November (SACCF).

It was found that the STF, SACCF and SBdy showed southwardshifts in their mean seasonal positions over the 15-year time-series. Apossible influence in this context may be the semi-annual oscillation(SAO). For example, Rouault et al. (2005) found that changes in thephasing of the SAO in recent decades has led to changes in seasonalwinds, temperatures and rainfall at Marion Island (48°S 38°E) in theIndian Ocean sector of the Southern Ocean.

The position and gradient of MADT at the various fronts was shownto be related in Table 2. An interesting question is why the positions ofthe fronts in the ACC shift northward (southward) in conjunctionwith astrengthening (weakening) in the SSH gradient, and why thisrelationship is reversed at the SBdy. The strength of the SSH gradientis an indicator of the degree of change in density fromonewatermass tothe next on either side of a given front. Additionally, the intensity ofconvergence–divergence of upper ocean waters is likely to influencethe strength of the SSH gradient. A product of such changes will un-doubtedly influence the frontal position.

Given that Colberg et al. (2004) found a strong impact of ENSO onthe mid-latitude South Atlantic, one might have expected a con-nection between this mode and the STF. It was found that the positionof the STF determined here is correlated with the ENSO MEI at 0.27with a lag of 12-months. Colberg et al. (2004) suggested that El Niñoshould cause an increase in the northward Ekman drift through anincrease in the mid-latitude westerlies. In agreement with thatsuggestion, the integrated SST data for the SAZ showed a coolingunder El Niño conditions. Caution however must be applied to theseresults. Not only does the South Atlantic response to the positive andnegative phases of ENSO appear to be nonlinear (Colberg et al., 2004)but ENSO also appears to have a lower frequency impact on SST in theSouthern Ocean than the SAM does (Sallée et al., 2008). It thereforeremains to be determined what is driving the interannual variabilityin frontal position and gradient observed at the Greenwich Meridian.

Our findings support previous studies that have shown that themid- and upper layers of the Southern Ocean have shown a warming

trend in recent decades, with the observed warming concentrated inthe ACC between 45°–60°S (Gille, 2002, 2008). Cooling (warming)trends to the north (south) of 1.3dyn m isoline in the ACC have beendocumented (Gille, 2002). Furthermore, the 15-year record analysedhere indicates that the frontal positions at the Greenwich Meridianhave remained stable or have shown a southward migration (STF,SACCF, and SBdy). The tendency towards a southward shift in theposition of the STF (Fig. 5) may be related to decreases in westerlywinds and a positive wind stress curl which, in turn, causes a greatersouthward influx of warm subtropical waters (e.g., Cai, 2006). Salléeet al. (2008) indicated that when the SAM is in a positive phase, asouthward shift in the APF in the Indian Sector of the Southern Oceanoccurs. However, we did not find that the APF at the GreenwichMeridian was strongly forced by the SAMwhen using altimeter or SSTdata during the 15-year time-series. The cooling of SST in the SBZ ofthe ACC can potentially be explained by the southward shift andincreased strength of westerly winds and negative wind stress curl(Cai, 2006). A southward shift and strengthening of the westerlywinds are associated with a positive phase in the SAM.

The results in the mean zonal SST data examined here also supportthe numerical modelling work of Sen Gupta and England (2006),which implies that increased northward Ekman transport occurs ataround 60°S, while an easterly intensification of winds at approxi-mately 40°S causes a poleward response in Ekman transport. Thisaspect is indicated by the immediate warming of SST in the SAZ andcooling in the SBZ. The changes in the Ekman transports duringpositive phases in the SAM are associated with weaker westerlies oreasterly winds at approximately 35°–40°S and strong westerly windsbetween 55° and 60°S (Sen Gupta and England, 2006). Sallée et al.(2008) noted that the fronts vary with latitude and are thereforeexposed to different Ekman transport anomalies induced by the SAM.Furthermore, there are strong regional differences in the SouthernOcean response of the SAM.

While both the SAM and the positions of the fronts at the GreenwichMeridian appear to showsimilar tendencies, the correlations foundhereare not significant. However, the SAM oscillates on all timescales fromsynoptic to centennial (Hall andVisbeck, 2002) and it is not clear atwhat

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temporal scales the various fronts within the ACC might respond toforcing from the SAM. Meredith and Hogg (2006) suggested a linkbetween the SAM Index and Eddy Kinetic Energy (EKE) in the ACC,witha two to three-year lag in the oceanic response to the SAM Index. Theyexplain that the lag is due to the time it takes for the signal of the SAM toreach the deep ocean layers. Moreover, the response of the ACC to theSAM is likely to vary with depth, with the upper ocean respondingquicker; thus, the lack of a strong correlation between the frontalpositions and the SAMmay relate to a mis-match in timescales and therelatively short time record available here.

In summary, our results indicate that the fronts in the SouthernOcean can be identified reasonably well bymeans of theMADT satellitedata giving positions that are consistent with those determined by Orsiet al. (1995) using hydrographic data. All of the five fronts show sub-stantial seasonal and interannual variability in these positions.

Acknowledgements

The Ssalto/Duacs MADT data set is distributed by Aviso, withsupport from CNES. MOI SST data are produced by Remote SensingSystems and sponsored by National Oceanographic PartnershipProgram (NOPP), the NASA Earth Science Physical OceanographyProgram, and the NASA REASON DISCOVER Project. Data are availableat www.remss.com. We thank G. Marshall at the British AntarcticSurvey and K. Wolter at NOAA for their provision of the SAM index(http://www.antarctica.ac.uk/met/gjma/sam.html) and theMEI index(http://www.cdc.noaa.gov/people/klaus.wolter/MEI/) respectively.W. Billany especially thanks S. Swart for support in this project andtechnical support from fellow postgraduate students at UCT, notice-ably Neil Hart and Natalie Burls. Funding for this study from the CHPC,Cape Town, South Africa is acknowledged. The work presented stemsfrom the taught MSc research project of the first author.

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