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VOLUME 22
NOTES AND CORRESPONDENCE
Nonstationary Impacts of the Southern Annular Mode on Southern Hemisphere Climate
GABRIEL SILVESTRI AND CAROLINA VERA
Centro de Investigaciones del Mar y la Atmósfera/CONICET-UBA, and DCAO/Facultad de Ciencias Exactas y Naturales,
Universidad de Buenos Aires, Buenos Aires, Argentina
(Manuscript received 15 January 2009, in final form 27 May 2009)
ABSTRACT
The temporal stability of the southern annular mode (SAM) impacts on Southern Hemisphere climate
during austral spring is analyzed. Results show changes in the typical hemispheric circulation pattern associated with SAM, particularly over South America and Australia, between the 1960s–70s and 1980s–90s. In
the first decades, the SAM positive phase is associated with an anomalous anticyclonic circulation developed
in the southwestern subtropical Atlantic that enhances moisture advection and promotes precipitation increase over southeastern South America (SESA). On the other hand, during the last decades the anticyclonic
anomaly induced by the SAM’s positive phase covers most of southern South America and the adjacent
Atlantic, producing weakened moisture convergence and decreased precipitation over SESA as well as
positive temperature anomaly advection over southern South America.
Some stations in the Australia–New Zealand sector and Africa exhibit significant correlations between the
SAM and precipitation anomalies in both or one of the subperiods, but they do not characterize a consistent
area in which the SAM signal can be certainly determined. Significant changes of SAM influence on temperature anomalies on multidecadal time scales are observed elsewhere. Particularly over the Australia–New
Zealand sector, significant positive correlations during the first decades become insignificant or even negative
in the later period, whereas changes of opposite sign occur in the Antarctic Peninsula between both subperiods.
1. Introduction
The leading mode of circulation variability in the Southern Hemisphere (SH) on low frequencies is the southern
annular mode (SAM, also referred as the Antarctic Oscillation or high-latitude mode). The SAM is characterized by a strong zonally symmetric pattern at polar
latitudes with a phase reversal at middle latitudes. Positive (negative) SAM phase is associated with negative
(positive) pressure anomalies over Antarctica and positive (negative) anomalies at middle latitudes. The pattern has been identified and discussed in many previous
studies (e.g., Rogers and van Loon 1982; Kidson 1988,
1999; Thompson and Wallace 2000) and plays an important role in the climate variability over different regions of the SH. Sen Gupta and England (2006), among
Corresponding author address: Gabriel Silvestri, CIMA/CONICETUBA, Intendente Guiraldes 2160, Ciudad Universitaria, Pabellón II,
2do, Piso (C1428EGA), Buenos Aires, Argentina.
E-mail: [email protected]
DOI: 10.1175/2009JCLI3036.1
Ó 2009 American Meteorological Society
others, show that the SAM positive phase during the
period 1979–2005 is mainly associated with negative
annual temperature anomalies over most of Antarctica
and Australia, with significant positive anomalies over
the Antarctic Peninsula, southern South America, and
southern New Zealand. During that particular period,
the SAM positive phase is also associated with negative
annual precipitation anomalies over southern South
America, New Zealand, and Tasmania and with positive
anomalies over much of Australia and South Africa.
Moreover, South American summer rainfall variability
associated with SAM is evident not only at interannual
(Zhou and Lau 2001) but also at intraseasonal time
scales (Carvalho et al. 2005). Recently, Ummenhofer
and England (2007) showed that New Zealand rainfall
variability is predominantly modulated by the ENSO
and SAM, with a latitudinal gradation in the influence of
the respective phenomena and a notable interaction
with orographic features. Among others, Hendon et al.
(2007) found that during austral winter (summer), SAM
positive phase is associated with decreased (increased)
15 NOVEMBER 2009
NOTES AND CORRESPONDENCE
daily rainfall over southeast and southwest Australia
(southern east coast of Australia). In addition, Reason
and Roualt (2005) found that six (six) of the seven (eight)
wettest (driest) winters during 1948–2004 occur in South
Africa during the negative (positive) SAM phase.
Southeastern South America (SESA) seems to be the
continental region in which SAM and precipitation
anomalies exhibit the strongest relationship in the period beginning around 1979 (Sen Gupta and England
2006). This relationship was particularly examined by
Silvestri and Vera (2003, hereafter SV03). Through the
analysis of the period 1979–99, SV03 found that SAM
influence is largest during austral spring, particularly
over the region encompassing northeastern Argentina,
southern Brazil, and Paraguay, approximately between
178 and 308S from 508 to 648W. The SAM positive
(negative) phase is associated with the intensification of
an upper-level anticyclonic (cyclonic) anomaly over the
southeastern Pacific Ocean, which gives rise to weakened (enhanced) moisture convergence and thus decreased (increased) precipitation over SESA. SV03 also
showed that during that particular season and period,
both SAM and ENSO indexes are significantly correlated (20.41, statistically significant at the 95% level
using a Student’s t test), which results in a strong modulation of the ENSO signal on SESA precipitation
anomalies by SAM activity. Nevertheless, Gillett et al.
(2006) computed the relationship between SAM and
precipitation anomalies considering a longer period
(1957–2005) and their results do not show the strong
SAM influence on precipitation anomalies observed in
SESA during more recent periods. From these previous
works it seems then that the relationship between SAM
and precipitation anomalies in SESA might not be stable.
There are some evidences of low-frequency variability
of precipitation over SESA. Rusticucci and Penalba
(2000) analyzed the precipitation regime over South
America south of 208S for the period 1901–90 and found
significant variations in the percentage of variance explained by the annual cycle on multidecadal time scales.
Variability with periods around 15–17 yr have also been
identified in river discharge anomalies in SESA (e.g.,
Robertson and Mechoso 2000; Berbery and Barros
2002). Nevertheless, the nature of the low-frequency
variability of precipitation anomalies over SESA is
neither fully described nor extensively understood yet.
Recently, Fogt and Bromwich (2006) examined the
decadal variability of the ENSO teleconnection to the
high-latitude South Pacific, also known as the Pacific–
South America (PSA) pattern. They showed notable
decadal changes in the SAM–ENSO correlation between
1979 and 2001. During the 1980s, the teleconnection was
weak due to the interference between the PSA pattern
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and the SAM. On the other hand, during the 1990s an inphase relationship between circulation anomaly responses associated with these two modes amplified the
height and pressure anomalies in the South Pacific,
producing stronger teleconnections. Fogt and Bromwich
conclude that the significantly positive correlation found
between ENSO and SAM only during times of strong
teleconnection suggests that both the tropics and high
latitudes need to work together for ENSO to strongly
influence the climate of the South Pacific sector.
The aim of this note is to analyze how stationary both
the SAM activity and its impact on Southern Hemisphere
climate is during austral spring [October–December
(OND)]. The note also focuses on better understanding
the low-frequency variability of precipitation anomalies
in SESA. The note is organized as follows: Data and
methodology are described in section 2; changes in the
SAM influence on precipitation, atmospheric circulation, and temperature anomalies during the last fifty
years are analyzed in section 3; and conclusions are
summarized in section 4.
2. Data and methodology
Monthly mean values of precipitation at 102 stations
and surface temperature at 103 stations located all around
the continental regions in the SH—available at Servicio
Meteorologico Nacional (SMN) of Argentina, the Data
Support Section of the Computational and Information
Systems Laboratory at the National Center for Atmospheric Research (NCAR) and the British Antarctic
Survey (BAS)—were used in the analysis. In addition,
precipitation anomalies over SESA were particularly
described through a precipitation index (PPindex), constructed averaging monthly mean rainfall anomalies at
seven stations located in the region where the most significant signal of SAM in austral spring precipitation was
found in SV03. The stations correspond to Asunción
(25.258S, 57.518W), Concepción (23.418S, 57.308W),
Villarrica (25.758S, 56.438W), Encarnación (27.318S,
55.838W), and Pilar (26.858S, 58.318W), from Dirección
de Meteorologı́a e Hidrologı́a (DINAC-Paraguay);
Iguazú (25.738S, 54.468W) from SMN; and Saenz Peña
(26.738S, 60.488W) from Instituto Nacional de Tecnologı́a
Agropecuaria (INTA-Argentina).
Hemispheric circulation anomalies were described by
monthly mean sea level pressure (SLP) at 95 stations
located around the SH, available from SMN, NCAR,
and BAS. Also, the analysis have been complemented
using monthly mean fields of SLP, 500-hPa geopotential
heights (Z500), and 850-hPa wind (WIND850) from the
National Centers for Environmental Prediction (NCEP)–
NCAR reanalysis (Kalnay et al. 1996).
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The SAMindex defined by Marshall (2003) (see http://
www.nerc-bas.ac.uk/icd/gjma/sam.html), based on sea
level pressure in situ observations, was considered. Sea
surface temperature anomalies in the El Niño 3.4 region
(EN34), available at the Climate Prediction Center
(http://www.cdc.noaa.gov/Correlation/nina34.data), were
considered as the ENSO index. The period of analysis
is from 1958 to 2004, which concentrates the largest
amount of information available for the variables under
analysis.
Anomalies have been defined as departures from the
corresponding OND seasonal climatological means computed over the 1958–2004 period. A linear trend was
also removed from all anomaly series considered. In addition, correlation values were calculated, ensuring that
the correlations were not dominated by few specific cases.
3. Results
a. ENSO and SAM
A significant relationship between SAM and ENSO
oscillations during austral spring has been described in
previous works (e.g., SV03; Fogt and Bromwich 2006;
L’Heureux and Thompson 2006), particularly over the
last 20 years of the twentieth century. The temporal
stability of such a relationship is further explored here
using correlation values between SAM and EN34,
computed over different subperiods of at least 22 years,
within the period 1958–2004 (Table 1a). It is evident that
the correlation between both indices over the entire
period is negligible. Significant correlation values of
negative sign are only found over the last decades, being
the maximum ones of those obtained for the last 22 years
considered. On the other hand, both SAM and ENSO
had independent activity during the first decades of the
period under study.
It is well known that the ENSO signal of precipitation
anomalies over SESA is strong (e.g., Aceituno 1988).
During austral spring, warm (cold) ENSO phases are associated with increased (decreased) precipitation over
SESA. The temporal stability of such relationships is explored in Table 1b. The correlation between the PPindex
and EN34index is significantly positive, not only over the
entire period but also for most of the subperiods considered. This result confirms that, in general, the ENSO influence on precipitation anomalies over SESA is relatively
stable. Accordingly, Garreaud and Battisti (1999) show
that interannual and interdecadal variability of the circulation anomalies in the SH associated with ENSO exhibit
similar spatial signatures in the SLP, low-level winds, and
temperature. Furthermore, previous works have identified interdecadal variation associated with ENSO (e.g.,
VOLUME 22
TABLE 1. Correlations of (a) the SAMindex–EN34, (b) PPindex–
EN34, and (c) PPindex–SAMindex with the effect of EN34 linearly
removed. Periods start (finish) in the years indicated in the first row
(column) of each table. One (two) asterisk(s) indicate correlations
statistically significant at the 90% (95%) for a Student’s t test.
1979
1984
1989
(a)
1958 0.04
0.05
0.15
1963
—
0.13 20.03
1968
—
—
0.09
1973
—
—
—
1978
—
—
—
1983
—
—
—
(b)
1958 0.41*
0.36*
0.36**
1963
—
0.15
0.27
1968
—
—
0.25
1973
—
—
—
1978
—
—
—
1983
—
—
—
(c)
1958 0.37*
0.19
0.18
1963
—
20.05 20.13
1968
—
—
20.32
1973
—
—
—
1978
—
—
—
1983
—
—
—
1994
0.07
20.07
20.21
20.32
—
—
0.41**
0.34*
0.16
0.01
—
—
0.03
20.16
20.37*
20.08
—
—
1999
2004
20.07
20.10
20.15
20.39*
20.41**
—
20.14
20.30*
20.42**
20.42**
20.49**
20.50**
0.43**
0.52**
0.40**
0.30*
0.47**
—
0.45**
0.52**
0.47**
0.43**
0.51**
0.51**
20.19
20.33**
20.50**
20.41**
20.55**
—
20.16
20.30**
20.47**
20.57**
20.63**
20.64**
Setoh et al. 1999). Nevertheless, the analysis of the influence of such variations on the climate variability in
South America is beyond the scope of this study.
The temporal stability of the relationship between
SAM and precipitation variability over SESA was also
explored. The ENSO signal was linearly removed from
PPindex and SAMindex (by a linear regression based on
EN34index) before the computation of the correlations
displayed in Table 1c. Results show that SAM and precipitation anomalies in SESA are independent when the
entire period is considered, in agreement with Gillett
et al. (2006). On the other hand, significant positive
correlations are found in the first decades (0.37 for 1958–
79) whereas significant negative correlations characterize the last decades (20.64 for 1983–2004).
The analysis of Table 1 shows a change in the relationship between SAM and both ENSO and precipitation anomalies over SESA from the first decades
(1958–79) to the last decades (1983–2004) of the period
considered. The issue whether such change is also noticeable in the climate variability over the entire SH is
further explored in the following section.
b. SAM signal on SH climate
In this section, correlation maps over the SH between
SAMindex and different atmospheric variables are analyzed. As described in previous sections, the ENSO signal
was linearly removed from all anomaly time series.
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NOTES AND CORRESPONDENCE
6145
FIG. 1. Correlations of the SAMindex with (a),(b) in situ precipitation, (c),(d) in situ SLP, (e),(f) reanalyzed SLP,
(g),(h) reanalyzed Z500, and (i),(j) in situ surface temperature for the periods (top) 1958–79 and (bottom) 1983–2004.
Correlations statistically significant at the 90% and 95% levels of a Student’s t test are shaded; gray dots in cases of
in situ observations indicate stations with no significant correlation.
Figures 1a and 1b show the correlation maps between
SAMindex and precipitation anomalies from ground
stations over the periods 1958–79 and 1983–2004. The
maps clearly depict the changes in sign and intensity of
the SAM influence on precipitation anomalies in SESA
between both subperiods described in the previous
section. The correlation between SAM and precipitation anomalies is also significantly negative (positive) in
1958–79 (1983–2004) at the few stations located in the
southern sectors of both Africa and Australia. However,
correlation values are insignificant at most of the stations for both periods, limiting considerably the determination of a consistent SAM signal over those two
regions. The analysis of the precipitation anomalies over
the Antarctic region was not included owing to the poor
quality of the data available, especially for the periods
before 1979. In agreement, Bromwich et al. (2000), exploring the interdecadal changes of ENSO signal in
Antarctic precipitation, found serious deficiencies in the
representation of the Antarctic net precipitation from
the reanalysis datasets.
The SAM signals on the circulation anomalies in the
SH for both subperiods were also analyzed in order to
identify changes in the circulation anomaly spatial patterns that might explain the changes observed for the
precipitation anomalies. In that sense, correlation maps
between the SAMindex and SLP and Z500 are shown in
Figs. 1c–h. In the case of SLP, correlation maps derived
from the NCEP–NCAR reanalysis are shown together
with those obtained from the 95 stations described in
section 2. The double analysis of SLP anomalies was done
because of the relatively low quality of the reanalysis in
depicting the climate variability, especially at the high
latitudes of the SH before 1979, mainly associated with
the lack of satellite information over the oceans.
Figures 1c–f show, in general for both subperiods, the
circulation anomaly pattern typically associated with
SAM and characterized by negative correlations at polar
latitudes and positive ones at middle latitudes. However,
a poleward migration of the correlation centers located
at middle latitudes, particularly over South America and
Australia, is noticeable between both subperiods. Similar spatial behavior of the SLP anomalies has been associated with an observed trend of the SAM toward
a more positive phase (see Marshall et al. 2006, and
references therein). Nevertheless, considering that the
anomalies used here have been previously detrended,
the results confirm that changes in the SLP anomaly
gradient between middle and high latitudes can also be
associated with natural low-frequency variability of the
climate system.
A more detailed analysis at regional scales shows that
most of stations located in the Australia and New Zealand
sector are significantly correlated with SAM in 1958–79,
but only few of them show significant correlation in 1983–
2004. On the other hand, no significant changes are evident in the SAM signature of the SLP anomalies in the
vicinity of Africa. Changes in the spatial structure of the
annular correlation center over the Antarctica are discernible between both subperiods from the reanalysis
but cannot be confirmed from in situ observations. Such
changes could be associated with the low quality of
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VOLUME 22
FIG. 2. Correlations of the SAMindex with SLP and regressions of the SAMindex– with
WIND850. Areas where correlations are statistically significant at the 90% (95%) for a Student’s t test are shaded in light (dark) gray.
the reanalyzed fields over Antarctica. Marshall (2003),
among others, shows that the quality of the NCEP–
NCAR reanalysis in the presatellite data is principally
poor at high southern latitudes.
In the vicinity of South America, the development of
a large positive correlation center over the southwestern Atlantic Ocean is particularly evident in 1983–2004
(Fig. 1f); it is weaker and located to the northeast in
1958–79 (Fig. 1e). The analysis of the corresponding
low-level wind anomalies confirms that during 1958–79
(Fig. 2a) positive SAM phases were associated with
anomalous southward wind anomalies over SESA induced by the anticyclonic circulation anomaly center
located in the southwestern subtropical Atlantic. Such
circulation enhances moisture advection and promotes
increase of precipitation over SESA, resulting in the
positive significant correlation between PPindex and
SAM depicted in Table 1c for that subperiod.
The correlation map between SAM and SLP anomalies
for the 1958–79 period was also made with 40-yr ECMWF
Re-Analysis, ERA-40, (not shown) to account for NCEP–
NCAR reanalysis uncertainties. Correlation maps from
both reanalysis datasets are, in general, very consistent.
Over South America, both reanalyses agree well in describing positive correlation centers over the Atlantic
and Pacific subtropical coasts. In particular, the positive
correlation for the Atlantic coast (leading to the precipitation anomalies) is similar in both reanalyses. The
main differences between both reanalyses take place
over Africa and eastern Antarctica. ERA-40 describes
positive significant correlations over most of southern
Africa that are not observed from NCEP–NCAR data.
Moreover, negative significant correlations cover the
entire Antarctic continent in ERA-40, while a wide
portion in the eastern Antarctic sector is described by
NCEP–NCAR reanalysis with no significant values.
During 1983–2004, the anticyclonic anomaly associated with the SAM positive phase is located farther
south, covering most of the southern tip of South
America and the adjacent Atlantic. This circulation
anomaly pattern is associated with weakened moisture
convergence (Fig. 2b) and decreased precipitation over
SESA (Fig. 1b), which justifies the negative significant
correlation between PPindex and SAM displayed in
Table 1c. In addition, Fig. 2b shows that the anticyclonic
anomaly pattern is related to negative correlations between precipitation anomalies over the southernmost
region of South America (Patagonia) and the SAMindex
(Fig. 1b). Under normal conditions, the Andes Mountains extending along the western coast force the ascent
of the westerly flow, causing abundant cloudiness and
precipitation in the surroundings of southern Andes
(e.g., Schwerdtfeger 1976). In that sense, the anomalous
anticyclonic circulation depicted in Fig. 2b weakens the
eastward flow, promotes subsidence conditions over the
area, and thus inhibits precipitation (Fig. 1b).
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NOTES AND CORRESPONDENCE
Correlation maps between SAM and Z500 are shown
in Figs. 1g,h and, in general, agree with the features
found for SLP. That confirms the equivalent barotropic
structure of the SAM-related circulation anomaly pattern described in previous publications (e.g., Thompson
and Wallace 2000). Correlation maps for Z500 during
1983–2004 (Fig. 1h) show a zonal wavenumber-4-like
pattern in middle latitudes that is absent in the corresponding map for 1958–79 (Fig. 1g). That feature is
partially discernible in the correlation map for SLP for
the same subperiod (Fig. 1f). However, owing to the lack
of enough upper-air stations along the SH over the entire period, it is not possible to verify whether the
wavenumber-4-like pattern was present in the presatellite period or not.
Previous works, such as Sen Gupta and England (2006)
among others, have described the SAM influence on
surface temperature anomalies in the SH, which seem to
be characterized by a SAM positive phase related to
negative temperature anomalies over the Antarctic continent and positive anomalies over the Antarctic Peninsula. Correlation maps between the SAMindex and surface
temperature anomalies for the two subperiods under
study are shown in Figs. 1i,j. Negative significant correlations are observed in New Zealand in 1958–79 but are
not significant in 1983–2004. In addition, one of the most
noticeable changes takes place in Australia. In fact, significant positive correlations cover most of the southern
Australian territory between 1958 and 1979, but the pattern changes considerably between 1983 and 2004 when
only four stations show significant correlation values of
negative sign. Over the Antarctica continent, except the
peninsula, negative correlations are observed in both
subperiods, although they are more significant in 1958–79
than in 1983–2004. In the vicinity of the Antarctic Peninsula, the correlations change notably between both
periods. Correlations are significantly negative during
1958–79, whereas they are significant and with positive
sign at only one station during 1983–2004. In addition,
significant positive correlation values covering most of the
Patagonia region are clearly identifiable during 1983–2004.
It is suggested that the anticyclonic circulation anomaly
observed over the southern continent and the adjacent
Atlantic during that period (Fig. 2b) might promote positive temperature anomaly advection into the region.
4. Conclusions
The nonstationary nature of SAM impacts on Southern Hemisphere climate during austral spring was explored in this note. A significant change in the spatial
circulation anomaly pattern typically associated with
SAM was found between the 1960s–70s and 1980s–90s.
A poleward migration of the anomaly centers located in
middle latitudes is noticeable between both periods,
especially over South America and Australia. In the first
decades, the development of an anticyclonic circulation
anomaly center in the southwestern subtropical Atlantic
associated with SAM positive phase is evident. That
circulation anomaly enhances moisture advection and
promotes precipitation increase over SESA. In contrast,
during the last decades the anticyclonic anomaly covers
most of the southern tip of South America and the adjacent Atlantic, producing a weakened moisture convergence and decreased precipitation over SESA as well
as positive temperature anomaly advection into the
Patagonia region.
A few stations in the Australia–New Zealand sector
and Africa exhibit significant correlations between SAM
and precipitation anomalies in both or one of the subperiods, but they do not characterize a consistent area
where the SAM signal can be explicitly determined. On
the contrary, there are changes in the influence of the
SAM on temperature anomalies elsewhere, such as the
change from a significant positive relationship over
southern Australia in the early period to a weaker negative relationship in the later period. Finally, changes in
the SAM–temperature anomaly relationship were observed in the Antarctic Peninsula—from negative in the
early periods to positive in the later period. This suggests
that changes in the temperature anomalies identified over
the Antarctic Peninsula could be also influenced by the
natural climate variability.
Acknowledgments. Comments and suggestions provided by two anonymous reviewers were very helpful in
improving this paper. This research was supported by
ANPCyT/PICT04-25269, CONICET/PIP-5400, and the
European Community’s Seventh Framework Programme
(FP7/2007-2013), under Grant Agreement 212492.
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