Southern Hemisphere Circulation and Relations with Sea Ice and

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JOURNAL OF CLIMATE
VOLUME 15
Southern Hemisphere Circulation and Relations with Sea Ice and Sea
Surface Temperature
JAMES A. RENWICK
National Institute of Water and Atmospheric Research, Wellington, New Zealand
(Manuscript received 31 May 2001, in final form 30 November 2001)
ABSTRACT
Relationships on the seasonal timescale between Southern Hemisphere 500-hPa height, sea surface temperature,
and Antarctic sea ice variability have been investigated using NCEP–NCAR reanalyses, NCEP sea surface
temperatures, and Met Office sea surface temperature and sea ice data. The dominant region of interannual
variability in the Southern Hemisphere circulation, over the southeast Pacific Ocean, is found to be related to
ENSO variability in tropical Pacific sea temperatures, as shown in a number of earlier papers. It is also related
to Antarctic sea ice variability, where an out-of-phase relationship is found between sea ice extent in the central
Pacific and in the southwest Atlantic Ocean. Sea ice extent is enhanced in one region when the atmospheric
flow anomaly is equatorward, presumably through a combination of anomalous heat flux and direct advection.
At the same time, the atmospheric flow anomaly in the other region tends to be poleward, resulting in a poleward
retreat in the sea ice edge. Such an interaction accounted for 63% of the total squared covariance between
hemispheric 500-hPa height and sea ice edge anomalies.
Averaged over the full data series used, no strong lag relationships were found, suggesting that circulation,
sea ice, and sea surface temperatures respond to one another on intraseasonal timescales. However, a composite
analysis with respect to the times of maxima or minima in Pacific sea ice extent did show apparently nonlinear
lag behavior. The negative height anomalies over the southeast Pacific associated with maxima in Pacific sea
ice tend to precede the ice maximum, or at least show no tendency to persist after the time of the ice maximum.
However, positive height anomalies over the southeast Pacific associated with minima in Pacific sea ice tend to
persist for some months after the ice minimum. The latter effect may be related to anomalous surface heat fluxes
associated with the upstream reduction in sea ice.
1. Introduction
In many respects, the mean large-scale circulation of
the Southern Hemisphere (SH) extratropics is strongly
zonally symmetric, brought about by the location and
shape of the Antarctic continent and associated sea ice
margins, and by the lack of significant other landmasses
south of 408S. On intraseasonal timescales, even the
distribution of variability of the circulation is relatively
zonal (Hurrell et al. 1998), exhibiting a maximum in
variance between 508 and 608S at most longitudes (e.g.,
Renwick 1998). A major component of seasonal to interannual variability in the SH is the high-latitude mode
(HLM; Kidson and Watterson 1999) also commonly
known as the Antarctic Oscillation (AAO; Thompson
and Wallace 2000), representing a zonally symmetric
exchange of mass between mid- and high southern latitudes.
On monthly and longer timescales, the variability of
the hemispheric circulation does, however, exhibit
Corresponding author address: James A. Renwick, NIWA, P.O.
Box 14901, Wellington, New Zealand.
E-mail: J.Renwick@niwa.cri.nz
q 2002 American Meteorological Society
asymmetry with a prominent center of low-frequency
variance over the southern Pacific. Across the Indian
and Atlantic Ocean sectors, variability on the synoptic
scale (10 days or less) is dominant (Renwick 1998). The
main reason for a concentration of low-frequency variability over the Pacific sector appears to be the influence
of teleconnections associated with the El Niño–Southern
Oscillation (ENSO; Kiladis and Mo 1998; Kidson
1999). Beyond atmospheric teleconnections, ENSO
modulates sea surface temperatures (SSTs) in midlatitudes and is known to influence Antarctic sea ice extent
in many places (Carleton 1989; Simmonds and Jacka
1995; Yuan and Martinson 2000), presumably as a result
of local atmospheric forcing. Sea ice variability plays
an important role in modulating regional heat budgets
(Stammerjohn and Smith 1997), implying that feedback
processes between sea ice and the atmospheric circulation may act to enhance low-frequency variability in
some high-latitude regions.
The purpose of this study is to investigate the leading
modes of seasonal variability in the SH extratropical
circulation and their relationship with Antarctic sea ice
and with SH sea surface temperatures. We seek to identify lead–lag relationships and to infer possible extra-
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RENWICK
tropical forcings on the SH tropospheric circulation.
Further, we also aim to identify possible mechanisms
for some observed relationships between ENSO and
Antarctic sea ice variability. Finally, we consider whether the localization of low-frequency circulation variance
over the southeast Pacific is partly associated with feedback processes between sea ice and the tropospheric
circulation.
Datasets and the statistical methods used in this study
are described in the following section. Results are presented in section 3, followed by a summary and conclusions in sections 4 and 5, respectively.
2. Data and methods
The tropospheric circulation in the SH extratropics is
analyzed in terms of a 42-yr time series (1958–99) of
monthly mean 500-hPa geopotential heights (H500)
from the National Centers for Environmental Prediction–National Center for Atmospheric Research
(NCEP–NCAR) reanalysis project (Kalnay et al. 1996).
Fields were projected from their original 2.58 latitude–
longitude resolution onto a 31-point by 31-point Southern Hemisphere polar stereographic grid covering all
latitudes south of 208S. The average grid spacing is
around 48 of latitude.
A number of authors have discussed the nature of
trends and possible discontinuities in the NCEP–NCAR
reanalyses (e.g., Kidson 1999; Hines et al. 2000). Before
any further analysis, a linear temporal trend was removed from the H500 fields at each grid point. The
approach and result is as discussed in Renwick and Revell (1999). After removal of the trend, a monthly mean
climatology was also removed, leaving the detrended,
deseasonalized anomalies. Questions may still remain
about the veracity of the H500 data at high southern
latitudes, especially over the sparsely observed southern
oceans. Despite this, the reanalyses represent our best
estimate of the atmospheric circulation in data-sparse
regions, being a dynamically consistent synthesis of
available global observations.
Monthly mean SST data were obtained from two
sources. Most use was made of NCEP optimally interpolated analyses generated from surface observations
and satellite estimates as described by Reynolds and
Smith (1994). The ‘‘Reynolds’’ data are defined on a 18
3 18 global latitude–longitude grid, from November
1981. Fields were subsampled onto a 28 3 28 grid covering latitudes 658S–608N, in the period November
1981–December 1999. Anomalies were calculated as
differences from the overall mean for each month. A
second set of SST data was obtained from the Met Office
(UKMO) Hadley Centre Sea Ice and SST dataset version
1.1 (HadISST1.0; Rayner et al. 1999). HadISST1.1 is
the latest in the series of datasets formerly known as
the Global Sea Ice and SST dataset (GISST; Rayner et
al. 1996). Fields were extracted for the period 1958–
98, on the same grid as used for the Reynolds data, and
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anomalies were calculated in the same way. The purpose
of using HadISST was to investigate decadal variability
in SST patterns of interest. However, as will be discussed later, before the advent of comprehensive satellite information in the early 1980s the HadISST data
appear to be of lower quality in some of the main extratropical regions of interest (especially across the Pacific south of ;408S). This makes such multidecadal
investigations of questionable value over the southern
oceans.
Sea ice information was also taken from the HadISST
dataset, only for the period from 1978 when passive
microwave information became available. Prior to 1978,
there is a strong reliance on climatological information,
making the sea ice data of little use for variability studies
(Rayner et al. 1999; N. Rayner 2001, personal communication). In HadISST, sea ice fields are defined as
fractional coverage in each 18 3 18 latitude–longitude
square. From this, a sea ice edge latitude was defined
at each longitude, by locating the maximum latitude at
which the sea ice concentration was at least 0.15. Where
no such value could be found, the sea ice edge was set
to the continental boundary. The procedure is analogous
to that used by Yuan and Martinson (2000). The sea ice
edge (SIE) latitude dataset was used in all subsequent
processing. As for SST, a monthly climatology was defined as the average over the full period of data, and
anomalies taken as the residuals from the climatology.
Although decadal-scale trends are known to exist in
Antarctic SIE (and in SST), no linear trend was removed
(see Yuan and Martinson 2000 for discussion of SIE
trends).
Much of the analysis was carried out using 3-month
(seasonal) means of each of the data series. Since the
SIE and main SST datasets cover only two decades, all
seasons of the year were used together, to maximize the
length of the time series and the statistical significance
of results. Such an approach is justifiable over the SH
on the basis that modes of atmospheric interannual variability in the SH show only small seasonal dependence
(e.g., Kiladis and Mo 1998). Moreover, extratropical SH
responses to tropical forcing show only weak seasonality (Trenberth and Caron 2000). However, the rather
large seasonal changes in extent and variability of Antarctic sea ice (Bromwich and Parish 1998) and its response to remote forcing (Simmonds and Jacka 1995)
may make such an approach inappropriate for SIE. This
issue is investigated and discussed later where appropriate.
Modes of variability within one field are identified
using empirical orthogonal functions (EOFs) and varimax rotation of EOFs (Wilks 1995). Modes of covariability between pairs of fields are identified using onepoint regression maps, compositing, and singular value
decomposition analysis (SVDA; Bretherton et al. 1992;
Renwick and Wallace 1996).
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3. Results
a. Variance structures
As a basis for much of the subsequent discussion, we
begin with the leading EOF patterns of monthly/seasonal H500 variability. The three leading varimax rotated EOFs (REOFs) of monthly mean H500 anomalies
are shown in Fig. 1. The rotation was based on the
leading 12 EOFs, though the form of the three leading
patterns is stable over a wide range of rotation dimensions (Renwick and Revell 1999). For seasonal (3
month) means, the three leading patterns are almost
identical in form to those shown in Fig. 1. On the monthly timescale, the modes account for 16%, 14%, and 8%
of the total height variance, respectively. For seasonal
means, the fractions of seasonal variance are 17%, 18%,
and 9%, respectively, the ordering of the leading two
modes reversing between monthly and seasonal averaging (as the averaging period increases further, REOF2
becomes relatively more important). The REOFs shown
in Fig. 1 appear to encapsulate much of the variability
described in the unrotated seasonal-mean EOFs of Mo
(2000), calculated from the same dataset.
The first REOF pattern is the well-known HLM/AAO,
related to zonally symmetric mass transfers between
mid- and high latitudes (Kidson and Watterson 1999;
Thompson and Wallace 2000). The second is concentrated over the southeast (SE) Pacific and has been associated with blocking occurrence in that region by Renwick (1998) and Renwick and Revell (1999). The third
pattern has a similar spatial structure to the second, but
with the main center of action over the southwest (SW)
Pacific. The second and third patterns may be considered
independently, or may both be seen as related to the
South Pacific wave train (Kidson 1999) or Pacific–South
American pattern (Mo 2000).
As noted above, increased temporal smoothing enhances the H500 variability over the SE Pacific relative
to other locations. Figure 2 shows the standard deviation
field for 3-month mean and for 25-month mean H500
anomalies. Even on the seasonal timescale, the SE Pacific region stands out, and it dominates the interannual
and longer timescale variance field. For example, the
region of the South Pacific enclosed by the 20-m contour
in Fig. 2 (bottom) contains approximately 5% of the
grid points south of 208S, but it accounts for nearly 25%
of the total interannual H500 variance.
Seasonal mean standard deviations for Reynolds SST
anomalies are illustrated in Fig. 3a. Standard deviations
are around 0.68C throughout much of the midlatitude
Pacific Ocean and decrease (and may become unreliable) toward the Antarctic coast. Over much of the global oceans, HadISST anomaly standard deviations were
comparable to those in Fig. 3a (for the matching period),
but were slightly lower than Reynolds values over the
southern oceans south of 508S. Sea surface temperature
information at high southern latitudes appears to be of
lower quality prior to the satellite era. The ratio of
FIG. 1. Covariance maps of (a)–(c) the leading three REOFs of
monthly mean 500-hPa height anomalies. The contour interval is 10
m. Negative contours are dashed, the zero line has been omitted.
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picture is similar for 2-yr (25-month) averages, with
higher variance across the Western Hemisphere rather
than the Eastern Hemisphere. In contrast to the H500
statistics, interannual SIE variance exhibits a peak in
the South Atlantic as well as across the South Pacific.
b. Coupled analysis
FIG. 2. The std dev (m) of averaged 500-hPa height anomalies, for
(top) 3-month averages and (bottom) 25-month averages. The contour
interval is 5 m, beginning from the 10-m contour.
HadISST standard deviations during the 30-yr presatellite period divided by those for the recent period is
shown in Fig. 3b. Variability is comparable or somewhat
higher in the early period over much of the globe, but
over the southern oceans and the southeast Pacific, there
appears to be a significant decrease in variance.
The climatology of Antarctic SIE location is illustrated in Fig. 4. Average sea ice extent is a minimum
in a broad region south of Australia, between ;908 and
1608E. There is another minimum through the Drake
Passage, north of the Antarctic Peninsula. Maximum
and minimum extent curves match well with those reported by Bromwich and Parish (1998) and others. The
longitudinal distribution of SIE anomaly variance mirrors that of mean extent, with a minimum in seasonal
variance south of Australia, a broad maximum across
the Pacific and a narrower peak in the Atlantic. Standard
deviation statistics are in broad agreement with the
monthly figures of Simmonds and Jacka (1995). The
A series of SST and SIE regression maps were calculated using the seasonal mean amplitude time series
of the three H500 REOF patterns (Fig. 1) and seasonal
mean Reynolds SST and HadISST SIE, at a range of
time lags. The amplitude time series of REOF1 shows
no significant linear relationship with either SST or SIE
at any lag from 0 to 1 yr (not shown). The lack of a
contemporary relationship with SST is in line with the
findings of Kidson and Watterson (1999) and Limpasuvan and Hartmann (2000) who suggest that on the
seasonal timescale, the high-latitude mode is internally
generated. The lack of a relationship with SIE is also
expected, since REOF1 represents almost solely a zonal
wind variation and is not associated directly with significant meridional flow or heat transport.
The nonlagged result for REOF2 (SE Pacific) is
shown in Fig. 5. The variance associated with the SE
Pacific REOF is clearly related to ENSO SST variability
in the tropical Pacific, as discussed by Renwick and
Revell (1999). There is also a more local SST signal
northwest of the main center of REOF2. Negative SST
anomalies centered near 508S, 1358W are associated
with enhanced equatorward flow during the positive polarity of REOF2. There is also evidence of positive SST
anomalies east of the main center of REOF2, around
the southern tip of South America. Such a pattern of
SST anomalies was also noted by Garreaud and Battisti
(1999). In the extratropics, values are significant around
New Zealand and near the center of the cool patch near
1358W (using the F test, 99% level, assuming one independent observation per year).
SE Pacific circulation variability is also related to SIE
across much of the Pacific and Atlantic, notably between
;1608 and 1208W, where the REOF2 amplitude accounts for around 25% of the variance in SIE. Regression coefficients are significant (F test, as above) in two
regions; from 1608 to 1108W and 858 to 558W. The sense
of the relationship is such that negative height anomalies
centered near 1208W are associated with equatorward
excursions of the sea ice edge to the west and poleward
excursions to the east. The implied circulation anomaly
has equatorward flow over the western and central South
Pacific, associated on average with equatorward transport of cold air (Kidson and Renwick 2002), and equatorward advection of sea ice, in the region of enhanced
SIE. Conversely, where the anomalous 500-hPa flow is
poleward (poleward transport of warm air), SIE is reduced. Comparing individual seasons, the regression
pattern shown in Fig. 5b remains essentially fixed
throughout the year. However, there is considerable sea-
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FIG. 3. (a) The std dev (8C) of 3-month mean Reynolds SST anomalies, with 0.28C contour interval,
and values less than 0.68C dashed; (b) ratio of the std dev of 3-month mean HadISST SST anomalies
during 1950–80 divided by those during 1981–98, with 0.2 contour interval, and values less than 1 dashed.
Values in (b) have been blanked out poleward of 608 lat.
sonal variation in amplitude, being largest in the months
of greatest sea ice extent (August–October) and least in
February–April.
The SW Pacific pattern (REOF3; Fig. 1c) exhibits a
weaker signal in the SST field (Fig. 6), but shares some
features with REOF2. There is a weak positive–negative
dipole west and east of the main center of REOF2, suggestive of local atmospherically forced surface heat fluxes. There is also a weak indication of ENSO forcing,
but in the opposite sense to that exhibited by REOF2,
consistent with the findings of Renwick (1998). Regression coefficients appear significant only near the
negative center south of New Zealand and near the positive center in the southeast Pacific (;608S, 1358W).
The pattern in SIE associated with REOF3 shows three
regions of influence. Regression coefficients are significant south of New Zealand (1408–1608E), across the
SE Pacific (1308–908W), and in the South Atlantic (508–
358W). The amount of variance accounted for is com-
parable in all three regions, around 12% south of New
Zealand and in the SE Pacific, and around 10% in the
Atlantic. In all three locations, the sense of the relationship is consistent with the implied anomalous circulation, as found for REOF2. As for REOF2, there is
seasonal variation in the amplitude of the SIE regression
pattern, but little change in form. The Pacific–Atlantic
SIE dipole response to REOF3 is similar to the reverse
of the REOF2 response (Fig. 5). Both REOF patterns
are associated with anomalous meridional flow in the
southeast Pacific–Atlantic sectors.
To explore which H500 patterns are most closely related to Pacific and Atlantic SIE variability, ice edge
anomalies were averaged over the sectors 1708–1308W
(Pacific) and 608–108W (Atlantic), the regions of largest
SIE response in Figs. 5b and 6b. The averaged SIE time
series were regressed upon H500 anomalies at each grid
point, as shown in Fig. 7. The resulting H500 patterns
are negatively correlated in space (20.51) and both are
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FIG. 4. Climatological SIE statistics. (top) The mean SIE location
in Feb (minimum, inner line) and Sep (maximum, outer line). SIE
std dev (degrees of lat) for (middle) 3-month averages and (bottom)
25-month averages.
spatially correlated with REOF2 (10.83 and 20.43, respectively). Both Atlantic and mid-Pacific SIE show the
strongest linear response to circulation variability over
the eastern South Pacific, near the Bellingshausen Sea.
Given the opposing polarities of the two regression patterns in Fig. 7, Pacific SIE tends to advance when Atlantic SIE recedes, and vice versa, as suggested in Figs.
5 and 6. Such behavior shows up clearly in an EOF
analysis of 3-month mean SIE anomalies. The leading
mode (not shown) accounts for 22% of the seasonal
variance and represents an out-of-phase oscillation between the mid-Pacific and the mid-Atlantic, with very
little amplitude elsewhere. Comparing the Pacific and
Atlantic SIE time series directly, the largest correlations
occur over the cool months (April–October) with Pacific
SIE leading Atlantic SIE by two months. Such a result
would be consistent with slow eastward propagation of
the H500 circulation feature, analogous to that found
by Kidson and Renwick (2002) for a matching feature
at 1000 hPa.
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The leading mode of an SVDA between seasonal
mean H500 and SST is shown in Fig. 8. It confirms the
result seen in Fig. 5, with a strong ENSO-like SST
pattern [correlation between the SST time series and
seasonally averaged Southern Oscillation index (SOI)
is 0.83]. The associated circulation pattern is similar to
the extratropical part of the ‘‘ENSO mode’’ described
by Kidson (1999) and is close to the form of H500
REOF2 (spatial correlation 0.75). In the SST field, the
negative anomaly in the southeast Pacific is again apparent, somewhat more prominent than in Fig. 5. An
SVDA between seasonal-mean H500 and SIE (Fig. 9)
also shows a leading H500 mode similar to REOF2
(spatial correlation 0.83) and an associated SIE pattern
similar to the regression map of Fig. 5. In both cases,
the leading SVDA mode dominates the covariance between H500 and SST/SIE, the squared covariance fractions (SCF) being 59% and 63%, respectively (H500
and SST/SIE pattern amplitude time series were correlated at 0.78 and 0.68, respectively).
The main center of action of the H500 pattern in Fig.
9 is somewhat southeast of the main center of H500
REOF2, and appears to be a compromise between the
location of the centers shown in Figs. 7a and 7b. The
relationship illustrated in Fig. 9 is strongest in the cool
months (for June–November, pattern amplitude time series correlation 0.82, SCF 63%) and weakest in the
warm months (for December–May, time series correlation 0.62, SCF 50%), but the form of the response
changes only slightly with season in concert with changing total sea ice extent.
A set of SVD analyses as described above were carried out at a series of time lags, from SST/SIE leading
H500 by two seasons (6 months) to H500 leading SST/
SIE by two seasons. For H500 and SST, it is well known
that ENSO SST anomalies force a wavelike response in
the extratropical circulation across the South (and
North) Pacific (Horel and Wallace 1981; Kidson 1999).
One might expect an SVDA with SST leading H500 to
exhibit the strongest coupling (in terms of SCF and
related statistics). However, there is no real indication
of lead–lag relationships between SST and H500, at
least on the monthly–seasonal timescale. Using lagged
SVDA, the SCF showed a minor (insignificant) peak
for SST leading by two months (62% versus 59% at
zero lag). The time series correlation for the leading
mode pair was close to constant across a range of lags
around zero. The leading spatial patterns were very similar to those shown in Fig. 8 for a wide range of lags.
While it is clear in physical terms that the extratropical
circulation pattern is a response to forcing by anomalous
tropical heating, the atmosphere responds quickly
enough that monthly or seasonal comparisons appear to
show no real lag (e.g., Renwick and Revell 1999).
Lagged SVDA between H500 and SIE also showed
relatively low sensitivity to time lag. As with SST, SCF
peaked insignificantly with SIE leading by one month
(64% versus 63% for no lag) and time series correlation
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FIG. 5. Regression maps based on seasonal (3 month) means of the SE Pacific time series (REOF 2
amplitude), with (a) Reynolds SST anomalies (contour interval 0.18C, negative contours dashed, zero
contour omitted) and (b) SIE. In (b), the regression coefficients (units are degrees of lat) have been
multiplied by 10 for clarity. Dark shading indicates positive values (ice increase) and light shading indicates
negative values (ice decrease). The increase/decrease has been plotted relative to the annual average
climatological SIE location. The 408, 508, 608, and 708S lat circles are shown in the background.
peaked slightly with H500 leading by one month (0.70
versus 0.69). This again suggests that on average one
field responds to the other on timescales shorter than
those considered here.
A composite analysis does however suggest an asymmetry in the H500–SIE relationship. Minima and maxima in Pacific SIE were identified as cases in the lowest
and highest quintiles (20 percentiles) of the mean SIE
distribution between 1708 and 1308W, the region used
for calculation of the regression map in Fig. 7a. Averaged H500 fields were calculated at a number of lags
relative to the SIE quintile dates (3-month periods). The
H500 averages for both SIE quintiles are shown in Fig.
10, for one season prior to the SIE extremes (top row),
at the time of the SIE extremes (middle row) and one
season after the SIE extremes (bottom row). When the
Pacific SIE anomaly is a maximum (farthest equatorward), the H500 anomaly tends to be relatively weak
and shows a tendency to lead the SIE anomaly. When
SIE is a minimum (farthest poleward), the H500 anomaly is relatively strong and tends to linger, with a positive
H500 anomaly evident for some months after the time
of the SIE minimum. For the Atlantic (not shown), SIE
anomaly maxima are associated with persistent positive
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FIG. 6. As in Fig. 5, but for the SW Pacific time series (REOF 3 amplitude).
H500 anomalies over the SE Pacific, while SIE minima
are on average associated with very weak H500 anomalies.
The magnitude of the H500 composite anomalies is
largest in the winter months, but the lag after the SIE
minimum is strongest over the summer months, when
the H500 anomaly has maximum amplitude one season
after the SIE minimum (not shown). The extremes in
SIE anomalies themselves are distributed through all
months of the year, but both minima and maxima tend
to occur more frequently in the summer months.
The difference in composite results may be related to
differences in surface heat fluxes brought about by presence/absence of sea ice and an associated nonlinear effect on the local circulation. Monthly mean surface heat
and radiation fluxes from the NCEP–NCAR reanalyses
were composited in the same way as the H500 fields,
but no coherent differences were seen across the far
South Pacific. However, in the reanalyses, elements of
the surface energy balance may not be depicted as reliably as are the large-scale wind and temperature fields,
especially in the data-sparse regions discussed here,
since they are dependent at least on model parameterizations of cloud effects and surface processes.
4. Discussion
A series of analyses have been carried out to relate
seasonal mean SH circulation to SST and Antarctic sea
ice edge variability. There is clear evidence for an ENSO
influence on sea ice across the Pacific and into the South
Atlantic, as found by Simmonds and Jacka (1995), Yuan
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FIG. 8. The leading mode of an SVDA between (top) 3-month
mean 500-hPa height anomalies and (middle) 3-month mean SST
anomalies, with no lag. Both contour plots are covariance maps based
on the leading SST time series. The contour interval is 5 m in (top)
and 0.18C in (middle). Negative contours are dashed and zero contours have been omitted. (bottom) The amplitude time series for the
500-hPa pattern (solid) and the SST pattern (dashed).
FIG. 7. Regression map between 3-month mean H500 anomalies
and (a) Pacific SIE anomalies averaged between 1708 and 1308W,
and (b) Atlantic SIE anomalies averaged between 608 and 108W. The
contour interval is 5 m, negative contours are dashed, and the zero
contour has been omitted.
and Martinson (2000), and others. It is apparently
brought about by ENSO-related wave propagation from
the subtropics resulting in anomalous high-latitude circulations centered over the southeast Pacific that are
associated with anomalous meridional heat fluxes and
with direct advection of the ice field.
Observed trends in SIE as reported by Stammerjohn
and Smith (1997) and Yuan and Martinson (2000) are
consistent with an increased frequency of positive H500
anomalies over the SE Pacific and an increased prevalence of blocking occurrence in that region over the past
20 years (Renwick 1998; Renwick and Revell 1999).
Recent trends in blocking occurrence are at least partly
related to the prevalence of El Niño events during the
1980s and 1990s. Over the last 40 years, however, there
appears to have been little systematic trend in SE Pacific
blocking frequency (Renwick and Revell 1999), implying that the opposing SIE trends over the east Pacific
and Atlantic sectors may not extend back through the
1960s and 1970s. Interdecadal changes in the frequency
and character of ENSO events, brought about by changes in the Pacific Decadal Oscillation (Mantua et al. 1997;
Power et al. 1999), will further modulate SIE trends,
through their effects on the atmospheric circulation
across the South Pacific.
On the timescales considered here, there is only a
weak indication that sea ice variability plays a role in
enhancing low-frequency circulation variability over the
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FIG. 9. (top) The leading mode of an SVDA between 3-month
mean 500-hPa height anomalies (contours) and 3-month mean SIE
anomalies (shading), with no lag. Both covariance maps are based
on the leading 500-hPa height time series. The contour interval is 5
m for 500-hPa height. Negative contours are dashed and the zero
contour has been omitted. The ice edge map has been multiplied by
10 for clarity and is plotted with respect to the annual mean climatological ice edge location. (bottom) The amplitude time series for
both fields, where 500-hPa height is solid, and ice edge is dashed.
South Pacific. A lack of sea ice across the western and
central South Pacific, partly related to anomalous poleward flow in the overlying atmosphere, appears to help
maintain the anomalous flow and associated positive
height anomalies over the region of largest low-frequency height variance. In the reverse situation, when
sea ice extent is enhanced across much of the South
Pacific, equatorward atmospheric flow anomalies may
precede the SIE anomalies, but the increase in sea ice
extent does not appear to help maintain the atmospheric
flow anomalies.
There is little evidence here of forcing of the atmospheric circulation by extratropical SST anomalies. The
leading SST–circulation coupled modes are strongly
ENSO related, related largely to remote forcing. To try
to isolate localized SST–circulation relationships, SVD
analyses were repeated after removal of a linear ENSO
signal (by regression against the SOI at each grid point,
at lags up to three seasons), and by using SST data
restricted to latitudes south of 358S. Removal of the
linear ENSO signal did help highlight local-scale relationships, resulting in a more prominent zonal wave-
3067
FIG. 10. Composites of seasonal (3 month) mean 500-hPa height
anomalies based on time of (left) maximum or (right) minimum average ice extent between 1708 and 1308W (as used in Fig. 6). (top)
Mean anomaly fields one season prior to the ice extremes, (middle)
mean anomaly fields for the same season the ice extremes, and (bottom) one season after the ice extremes. The contour interval is 10
m, negative contours are dashed, and the zero contour has been omitted.
number-3 pattern in H500 (not shown) and matching
same-sign SST anomalies. No preferred lag was evident
after removal of the ENSO signal, with the H500/SST
mode time series correlation highest near zero lag. If
anything, such a result is indicative of forcing of SST
anomalies by the atmospheric circulation, as found by
many authors (e.g., Basher and Thompson 1995).
Acknowledgments. The author would like to thank Dr.
John Kidson for helpful discussions on the Southern
Hemisphere circulation, and for reviewing an earlier
version of the manuscript. The Reynolds SST and the
reanalysis data were made available through the NCAR
Data Services Section. Many thanks to Dr. Nick Rayner
of the Met Office, Hadley Centre for provision of and
assistance with HadISST data. The review comments of
Dr. Nick Rayner and Dr. Ian Simmonds were very help-
3068
JOURNAL OF CLIMATE
ful in improving the completeness and presentation of
results. This research was funded by the New Zealand
Foundation for Research, Science and Technology under
Contract C01X0030.
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