INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 33: 1948–1963 (2013)
Published online 20 July 2012 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3563
Temperature changes in the mid- and high-latitudes
of the Southern Hemisphere
Y. Richard,a * M. Rouault,b,c B. Pohl,a J. Crétat,a I. Duclot,a S. Taboulot,d C. J. C. Reason,b
C. Macrona and D. Buirona,e
a
c
Centre de Recherches de Climatologie, UMR6282 Biogéosciences, CNRS/université de Bourgogne, Dijon, France
b Department of Oceanography, Mare Institute, University of Cape Town, Rondebosch, South Africa
Nansen-Tutu Centre for Marine Environmental Research, James Building, University of Cape Town, Cape Town, South Africa
d Météo-France, Dijon, France
e Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS/Université Joseph Fourier, Grenoble, France
ABSTRACT: A Hierarchical Ascending Classification is used to regionalize monthly temperature anomalies measured
at 24 weather stations in Antarctica and the Sub-Antarctic and mid-latitude southern islands from 1973 to 2002. Three
principal regions are identified that are geographically coherent: Eastern Antarctica, the Antarctic Peninsula and the SubAntarctic and mid-latitude islands. Within each region, consistent trends are observed: namely, stationary temperatures in
‘East-Antarctica’; a robust warming in the ‘Sub-Antarctic and mid-latitude islands’, most pronounced in austral summer
(nearly 0.5 ° C per decade); and a strong but more recent warming in the ‘Antarctic Peninsula’. Austral summer temperature
anomalies are related to (1) the Southern Annular Mode (SAM) indexes computed using two reanalysis products (20th
Century Reanalyses and ERA40) over two periods (1958–2002 and 1973–2002), (2) the seasonal frequencies of four
recurrent daily weather regimes identified with a k-means algorithm applied on the 500hPa geopotential height (DJF
1958–2002) and (3) HadSST2 sea surface temperature (SST) anomalies (DJF 1958–2002). East-Antarctica interannual
temperature anomalies are associated with the SAM variability. In the Antarctic Peninsula, only the long-term trend is
common with the SAM. The SAM does impact significantly the temperature anomalies of the Sub-Antarctic and midlatitude islands. Trend and interannual variability of the islands’ temperatures are associated with the nearby SST. For the
Indian Ocean stations, warming in the Agulhas Current system could also have led to these changes. Copyright  2012
Royal Meteorological Society
KEY WORDS
climate change; temperature; Southern Hemisphere; regionalization; Sub-Antarctic islands; Southern Annular
Mode; sea surface temperature
Received 11 July 2011; Revised 13 April 2012; Accepted 23 June 2012
1.
Introduction
Since the 1950s, the Southern Ocean has experienced a
stronger atmospheric circumpolar flow around Antarctica,
a weaker westerly flow in the mid-latitudes (Thompson
et al., 2000) and a strong oceanic warming (Gille, 2002,
2008). Substantial ice mass loss inferred using radar
interferometry has occurred over the Antarctic Peninsula
(Rignot et al., 2008). The recent warming recorded there
is unprecedented over the last two millennia (Vaughan
et al., 2003). Gillett et al. (2008) and Monaghan and
Bromwich (2008) found that such observed changes in
Antarctic temperature are not consistent with internal climate variability or natural climate drivers alone, and are
directly attributable to human influence. More recently,
Qu et al. (2011) highlight how the anthropogenic intensification of global hydrological cycle induces a strong
increase of the latent heat transport into the Antarctic
* Correspondence to: Y. Richard, Centre de Recherches de Climatologie, CNRS/Université de Bourgogne, 6 Bd. Gabriel, 21000 Dijon,
France. E-mail: [email protected]
Copyright  2012 Royal Meteorological Society
Peninsula, which explains the main part of the significant warming observed in this region through the 20th
century. Polvani et al. (2011) suggest that most Southern
Hemisphere tropospheric circulation changes, in austral
summer and over the second half of the 20th century,
have been caused by polar stratospheric ozone depletion.
Due to a paucity of lands and in situ data, the SubAntarctic region has received less attention than Antarctica. Yet, meteorological stations were installed in the
Sub-Antarctic Islands as early as the 1950s. Marion
Island climate (46.88 ° S, 37.85 ° E) has undergone significant changes since the 1960s, mostly in austral summer
(Rouault et al., 2005). They consist of a large decrease
in rainfall and increases in sea level pressure, maximum
and minimum local air temperature and near-shore sea
surface temperature (SST). Farther east, Chapuis et al.
(2004) reported that the annual mean air temperature at
Kerguelen Island (49.35 ° S, 69.22 ° E) has increased by
1.3 ° C since the mid-1960s. About 433 km to the southeast, at Heard Island (53.10 ° S, 73.51 ° E), further evidence
of climate change comes from widespread glacier retreat
1949
TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
(Thost and Truffer, 2008). All these changes led to deep
modifications in the local ecosystems (Inchausti et al.,
2003; Pakhomov et al., 2004; Smetacek and Nicol, 2005;
Bergstrom et al., 2006; Chown and Froneman, 2008;
Le Roux and McGeoch, 2008a, 2008b; Nyakatya and
McGeoch, 2008).
In spite of the importance of such modifications, explanations for temperature changes in the mid- and highlatitudes of the Southern Hemisphere remain incomplete.
The Antarctic Peninsula and other islands are known to
have warmed up substantially in the past decades but
a clear synthesis of the geography, the timing or the
characteristics (trend or breaking) of the warming is still
missing, not only on the Antarctic continent, but also on
the islands in the surrounding Southern Ocean. What are
the seasonal components of the warming? What are the
synoptic weather regimes associated with such temperature changes?
To date, The Southern Ocean as a whole (Antarctica
coast, Sub-Antarctic and southern mid-latitude island
time series) has not been analysed together (Gillett et al.,
2006). The first aim of the present work is to document
the spatial coherence of observed temperature changes
there. We focus on austral summer, the season that is
most sensitive to temperature changes. The second aim
is to quantify to what extent changes noted in observed
in situ air temperatures relate to changes in synoptic-scale
weather regime occurrences and/or trends in SST and
Southern Annular Mode (SAM).
2. Data
ensemble Kalman filter, ensuring consistency between the
pre-satellite and the satellite era. 20CR fields used here
are the ensemble mean of 56 members. Although surface
data were assimilated since the first year of the reanalysis in the southern mid-latitudes, the first data assimilated in Antarctica date back from the early 1910s, with
a 30 year gap between the two World Wars. Although
in situ observed data remain rare in the Southern Hemisphere even in recent years, the station network is denser
and almost constant since the International Geophysical Year (1957) only. Over the Southern Ocean, a specific issue concerns the non-consideration of the interannual sea-ice extent variations. Reanalyses are more
reliable in summer when the jet stream does not shift
significantly regardless of whether the sea-ice edge is
extended or contracted (Kidston et al., 2011). In winter, their reliability increased during the satellite era
(even for the 20CR), due to better monitoring of sea-ice
extent. In this work, the 20CR are therefore used over
the 1958–2002 period, focusing on the austral summer
season.
2.2.
The HadSST2 dataset
The UK Meteorological Office Hadley Centre’s SST
dataset version 2 (HadSST2) is a monthly global SST
dataset provided on a 5° × 5° regular grid since 1850
(Rayner et al., 2006). This dataset is only based on
observations and does not interpolate surface temperature
over the regions that could not be documented. It is thus
thought to minimize statistical artefacts in the southern
high-latitudes, where in situ measurements are rare.
2.1. The ERA40 and 20th Century Reanalyses
The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA40 reanalysis (Uppala et al., 2005),
available from 1958 to 2002, was used to document the
large-scale circulation and atmospheric configurations.
Marshall (2003) and Bromwich and Fogt (2004) compared ERA40 and NCEP/NCAR reanalysis with Antarctic and other mid- to high-latitude station observations
from 1958 to 2001. They found that ERA40, a secondgeneration reanalysis that assimilated many Antarctic stations from the start of the run, was generally more in
agreement with in situ observations. Prior to the late
1970s, the quality of the reanalyses depends on the season: Bromwich et al. (2007) indicate that they are only
reliable during summer.
To ensure the robustness of the results and take into
account the uncertainties identified by Bromwich and
Fogt (2004): ‘a more detailed look at the presatellite
era reveals many shortcomings in ERA40, particularly
in the Southern winter’, the same tests were also conducted with the 20th Century Reanalysis version 2 (20CR
hereafter), a dataset with time-consistent data assimilation (Compo et al., 2011), recently used to document
decadal changes in the region (Pohl and Fauchereau,
2012). 20CR are available since 1871 on a 2° × 2° regular grid, and assimilate only surface data through an
Copyright  2012 Royal Meteorological Society
2.3.
An original and international temperature dataset
Provided by numerous weather services (Météo-France,
the South African Weather Service, the British Antarctic Survey Reader, the New Zealand National Institute
of Water and Atmospheric Research and the Australian
Government Bureau of Meteorology), 24 monthly temperature time series were compiled for this study (Table I,
Figure 1). They document the mid- to high-latitude temperatures of the Southern Ocean, except over the South
Pacific where there is no measurement. The dates from
which data are available vary from 1950 to 1974 and
most of these series include missing values (from 0 to
11.6%, and 2.9% on average, between their opening date
and 2002: see Table I).
2.4.
The Marshall SAM index
To compare our results with a recognized index of
the SAM, we consider the Marshall monthly index
(http://www.antarctica.ac.uk/met/gjma/sam.html) documenting the meridional pressure gradient between 40 ° S
and 65 ° S. This index was computed with observed sea
level pressure at 12 stations using the methodology of
Marshall (2003).
Int. J. Climatol. 33: 1948–1963 (2013)
1950
Y. RICHARD et al.
Table I. Summary of the monthly temperature records.
Meteorological
station name
Antarctica situation
or Island (Is.)
Area or
ocean
Country of
meteorological
survey
Latitude
( ° S)
Amsterdam
Arturo Prat
Bellingshausen
Casey
Chatham
Crozet
Davis
Dumont d’Urville
Esperanza
Faraday/Vernadsky
Gough
Halley
Kerguelen
Macquarie
Marambio
Marion
Mawson
Mirny
Molodeznaja
Novolazarevskaia
O’Higgins
Orcadas
Rothera
Syowa
Amsterdam Is.
South Shetland Is.
King George Is.
Vincennes Bay
Chatham Is.
Crozet Is.
Princess Eliz. Land
Adélie Land
Hope Bay
Galindez Is.
Tristan da Cunha Is.
Halley Bay
Kerguelen Is.
Tmacquarie Is.
Marambio Is.
Prince Edward Is.
Mc Robertson Land
Davis Sea
Cosmonaut Sea
Queen Maud Land
North end Peninsula
Orcadas Is.
Adelaide Is.
East Ongul Is.
Indian Ocean
Ant. Peninsula
Ant. Peninsula
East Antarctic
Pacific Ocean
Indian Ocean
East Antarctic
East Antarctic
Ant. Peninsula
Ant. Peninsula
Atlantic Ocean
West Antarctic
Indian Ocean
Pacific Ocean
Weddel Sea
Indian Ocean
East Antarctic
East Antarctic
East Antarctic
East Antarctic
Ant. Peninsula
Ant. Peninsula
Ant. Peninsula
East Antarctic
France
Chile
Russia
Australia
New Zealand
France
Australia
France
Argentina
UK/Ukrania
South Africa
UK
France
Australia
Argentina
South Africa
Australia
Russia
Russia
Russia
Chile
Argentina
UK
Japan
37.8
62.5
62.2
66.3
44.0
46.4
68.6
66.7
63.4
65.4
40.4
75.6
49.3
54.5
64.2
46.8
67.6
66.5
67.7
70.8
63.3
60.7
67.5
69.0
Longitude
(° )
77.5 E
59.7 W
58.9 W
110.5 E
176.6 E
51.9 E
78.0 E
140.0 E
57.0 W
64.4 W
9.9 W
26.6 W
70.2 E
158.9 E
56.7 W
37.8 E
62.9 E
93.0 E
45.9 E
11.8 E
57.9 W
44.7 W
68.1 W
39.6 E
Altitude
(m)
Start
date
Gaps
(%)
27
5
16
42
44
146
13
43
13
11
54
30
29
8
198
24
16
30
40
119
10
6
16
21
1950
1960
1968
1957
1956
1974
1957
1956
1960
1950
1956
1957
1951
1948
1970
1951
1954
1956
1963
1961
1963
1950
1977
1957
0.4
0.0
0.4
0.6
11.6
4.4
9.0
0.2
4.9
3.5
0.5
0.0
0.0
5.2
4.9
2.9
0.8
0.5
1.6
0.5
2.2
4.0
1.0
10.3
Figure 1. Observed in situ data. Shaded areas correspond to the Hierarchical Ascending Classification results.
Copyright  2012 Royal Meteorological Society
Int. J. Climatol. 33: 1948–1963 (2013)
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TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
80
60
3
40
6
20
Nb. of Classes
Euclidean Distance
100
Be
llin
Ar
gs turo
ha
us
e
Hi n
gg
i
Or ns
ca
d
Fa as
ra
da
Ro y
t
Es hera
pe
re
Ma nza
ra
Am mb
ste io
r
Ke dam
rg
Ma uele
cq n
ua
r
Cr ie
oz
e
Ma t
rio
Go n
u
Ch gh
ath
am
Ha
lle
West-Antarctic C y
as
ey
Mi
rn
Du y
mo
nt
Da
Ma vis
ws
M
No olo
on
de
vo
zn
laz
a
ar
ev ja
sk
aia
Sy
ow
a
0
Antarctic Peninsula
Sub-antarctic &
mid-latitude islands
East-Antarctic
Figure 2. Clustering tree of the Hierarchical Ascending Classification applied to normalized monthly temperature anomalies.
3. Regionalization of monthly temperature
anomalies
3.1. Methodology: the Hierarchical Ascending
Classification
We attempt first to regionalize the temperature evolutions. A Hierarchical Ascending Classification (HAC)
algorithm is applied to the 24 monthly series to identify which stations can be objectively grouped together
according to their similarities. To exclude the seasonality, the classification is applied over monthly standardized
temperature anomalies. The ascending nature of the HAC
technique means that, at the start of the algorithm, each
station constitutes a separate class (Figure 2). Then, iteratively, the algorithm groups two by two all the classes
(i.e. station or group of stations) until a single class,
agglomerating all the stations, is obtained. At each step,
the algorithm identifies the two most similar classes. The
criterion used to aggregate the most similar classes is
Ward’s method (Ward, 1963), based on Euclidean distances, and also called minimum variance clustering. It
is based on the minimization of the intra-class inertia
(i.e. it minimizes the heterogeneity between stations of a
given class).
The HAC does not support missing values. Before
(since) 1973, missing values are frequent (sparse) for
most of in situ temperature series. Usual methods of
recovery data (e.g. regression) are often based on neighbouring series. In our study, the stations are often very far
from each other, which make this approach inappropriate. Hence, an alternative method is applied, consisting
to relate observed values to large-scale atmospheric patterns. We filled monthly temperature gaps through simple
linear regressions with ERA40 temperature at the nearest
grid point. Following Bromwich and Fogt (2004), who
noticed abrupt shifts in the 1960s due to data assimilation inconstancies, we restricted our data reconstruction
(and thus our HAC analysis) to the period 1973–2002.
Copyright  2012 Royal Meteorological Society
3.2. Spatialization of temperature evolutions
One cluster highlights a specific signal to the Antarctic Peninsula and neighbouring islands (Figure 2). The
associated eight ‘Antarctic Peninsula’ stations (Table I,
Figure 1), i.e. Arturo, Bellingshausen, O’Higgins,
Orcadas, Faraday, Rothera, Esperanza and Marambio,
form unsurprisingly a coherent class in terms of monthly
temperature anomalies (Figure 2). The 16 remaining stations are discretized into two well-separated regions, the
‘East-Antarctic’ region (Casey, Mirny, Dumont d’Urville,
Davis, Mawson, Molodeznaja, Novolazarevskaia and
Syowa) and the ‘Sub-Antarctic and mid-latitude islands’
(Amsterdam, Kerguelen, Macquarie, Crozet, Marion,
Gough, Chatham), to which must be added Halley, the
only ‘West-Antarctic’ station. It is located on the Brunt
Ice Shelf floating on the Weddell Sea, and experiences a
coastal ice shelf climate, isolated by the HAC when at
least four classes are considered. It has previously been
identified as experiencing a different climate from that of
the Antarctic Peninsula (Turner et al., 2005; Steig et al.,
2009).
Some results were expected, e.g. the consistency of
the Antarctic Peninsula and the clear predominance
of geographical proximity in the determination of the
classes. These results nonetheless reflect the existence
of specific regional characteristics and give confidence
in the reliability of the data and the usefulness of the
HAC. Others results are novel, particularly the SubAntarctic and mid-latitude islands coherency, despite the
fact that these islands are separated by several thousands
of kilometres. For these stations a latitudinal logic
prevails. However, given that the rare (<5%) missing
values were filled from ERA40, this could contribute
to exaggerate the similarities between stations. The
following analyses will therefore use only data without
reconstruction, in order to assess the robustness of the
classification and to detail the characteristics of each
cluster.
Int. J. Climatol. 33: 1948–1963 (2013)
1952
4.
Y. RICHARD et al.
Analysis of temperature trends
To test and detail the results of the HAC (Section 3), we
perform an analysis for each station, on a longer period
(1958–2002), and without data reconstitutions. Trends at
the 24 stations (grouped according to the HAC regions:
Figure 3(a)–(c)) are also analysed for each month.
Trends (parabolic curves) and breaks in stationarity (broken curves) are identified using a nonparametric Pettitt
test, which is based on the Mann–Whitney test (Pettitt,
1979). In the Sub-Antarctic and mid-latitude islands
5
−5
1960 65
70
75
80
85
90
°C
Kerguelen (Central Indian O.)
−5
0
0
1960 65
70
75
80
85
90
95 2000
Macquarie (Pacific O.)
−5
−5
5
0
1960 65
70
75
80
85
90
95 2000
−5
Chatham (Pacific O.)
°C
70
75
80
85
90
95 2000
1960 65
70
75
80
85
90
95 2000
80
85
90
95 2000
85
90
95 2000
Gough (Atlantic O.)
0
5
0
0
1960 65
70
75
80
1960 65
70
75
Halley (West Antarctic)
5
−5
1960 65
Amsterdam (Central Indian O.)
5
5
°C
95 2000
5
−5
Crozet (West Indian O.)
0
0
STD
°C
5
(a)
Marion (West Indian O.)
(Figure 3(a)), the Pettitt test detects non-stationary temperatures for six stations: the four Indian stations (Marion, Crozet, Kerguelen and Amsterdam) and the two
Pacific islands (Macquarie and Chatham). This signal
takes the form of linear warming trends, without sudden
ruptures, as shown by the parabolic curves of the Pettitt
test. Their extrema indicate that the warming is particularly early (1960s and 1970s), especially at Macquarie
and Amsterdam, and strong in the South Indian Ocean
islands (Marion, Kerguelen and Amsterdam). The warming trends remain even significant in the two shorter or
85
90
95 2000
−5
1960 65
70
75
80
Figure 3. Monthly temperature anomalies (solid black curves), Pettitt tests (red curves) and associated confidence level (blue: 95%; red: 99%),
for (a) Sub-Antarctic and mid-latitude islands class, (b) Antarctic Peninsula class and (c) East-Antarctic class.
Copyright  2012 Royal Meteorological Society
Int. J. Climatol. 33: 1948–1963 (2013)
1953
TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
°C
5
(b)
Arturo
5
0
−5
0
1960 65
70
75
80
85
90
95 2000
−5
°C
O-Higgins
5
0
0
1960 65
70
75
80
85
90
95 2000
−5
°C
Faraday
5
0
0
1960 65
70
75
80
85
90
95 2000
−5
°C
Esperanza
5
0
0
1960 65
75
80
85
90
95 2000
1960 65
70
75
80
85
90
95 2000
70
1960 65
70
75
80
85
90
95 2000
70
75
80
85
90
95 2000
Marambio
5
−5
70
Rothera
5
−5
1960 65
Orcadas
5
−5
Bellinshausen
75
80
85
90
95 2000
−5
1960 65
Figure 3. (Continued ).
incomplete series (Crozet and Chatham). Gough Island,
the only record in the Atlantic sector, presents a small
shift (sudden warming near 1973) but no significant trend
overall. There is no significant temperature change in
Halley (i.e. the West-Antarctic station). Over the Antarctic Peninsula (Figure 3(b)), warming trends are significant at almost all stations. Although differences between
the time period covered by the eight series do not allow
us to describe with confidence the common trend, it
seems that warming starts later over Antarctic Peninsula
(maximum in the 1980s) than in the Sub-Antarctic and
Copyright  2012 Royal Meteorological Society
mid-latitude islands. No station of the Antarctic Peninsula
reaches statistical significance (according to the Pettitt
test) as clearly as in the South Indian Ocean (Marion,
Kerguelen and Amsterdam). The East-Antarctic region
(Figure 3(c)) differs from the previous two, in that seven
of eight stations do not experience any significant change.
Only Novolazarevskaia, the westernmost station, experienced a significant warming.
To complete these analyses, linear adjustments are
computed to quantify the annual and monthly warming (Table II). Annual mean temperature exhibits linear
Int. J. Climatol. 33: 1948–1963 (2013)
1954
Y. RICHARD et al.
(c)
°C
Casey
5
0
0
−5
°C
5
5
°C
1960 65
70
75
80
85
90
95 2000
Dumont d’Urville
5
1960 65
70
75
80
85
90
95 2000
Mawson
−5
5
70
75
80
85
90
95 2000
70
75
80
85
90
95 2000
70
75
80
85
90
95 2000
70
75
80
85
90
95 2000
Davis
1960 65
Molodeznaja
0
1960 65
70
75
80
85
90
95 2000
Novolazarevskaia
−5
5
1960 65
Syowa
0
0
−5
1960 65
0
0
−5
−5
5
0
−5
°C
Mirny
5
1960 65
70
75
80
85
90
95 2000
−5
1960 65
Figure 3. (Continued ).
warming trends in all stations of the Antarctic Peninsula region. The trends range from +0.22 (O’Higgins)
to +0.72 ° C per 10 year (Rothera). Warming dominates
from January to August, in agreement with previous
results (Jacka and Budd, 1998; Steig et al., 2009; Qu
et al., 2011). It is hardly perceptible in spring (from
September to December), confirming once again Jacka
and Budd (1998). In contrast, among the East-Antarctic
region, six stations do not show significant temperature trends. Only Novolazarevskaya and Casey have
trends significant at the 99 and 90% level, respectively.
Copyright  2012 Royal Meteorological Society
The distinct seasonal trends identified by Schneider
et al. (2011), with some cooling in summer and autumn
contrasting with warming in winter and spring, are
often non-significant. The isolated warming of July
(when the weather makes measurements difficult) in
Novolazarevskaia (as in Molodeznaja) raises the question
of data reliability. In the Sub-Antarctic and mid-latitude
islands region, the warming ranks from +0.20 per 10 year
(Amsterdam) to +0.28 ° C per 10 year (Marion). Farther
east, south of Tasmania (Macquarie) and in the Pacific
Ocean (Chatham), warming remains significant but is
Int. J. Climatol. 33: 1948–1963 (2013)
1955
TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
Table II. Linear annual and monthly temperature trends ( ° C/10yr).
Station\Date
Yr
Arturo Prat
.33+++
Bellingshausen
.23+++
O’Higgins
.22+++
Orcadas
.23+++
Faraday/Vernadsky
.53+++
Rothera
.72+++
Esperanza
.32+++
Marambio
.48+++
Amsterdam
.20+++
Kerguelen
.21+++
Macquarie
.11+++
Crozet
.21+++
Marion
.28+++
Gough
.03
Chatham
.15+++
Halley
−.09
Casey
.10+
Mirny
.08
Dumont d’Urville
.06
Davis
.08
Mawson
−.03
Molodeznaja
−.01
Novolazarevskaia
.21+++
Syowa
.04
J
F
M
A
.35+++
.36+++
.09
.26+++
.29+++
.36++
.33++
.66+++
.30+++
.37+++
.19++
.54+++
.44+++
.09
.29++
.13
−.10
−.19
.00
.05
−.07
−.25
.17
−.07
.45+++
.28++
.28+++
.29+++
.30+++
.50+++
.75+++
.36++
.31+++
.32+++
.20++
.41++
.42+++
.00
.18
−.11
−.07
−.04
.08
.09
−.04
.06
.15
.16
.44+++
.26+
.25+
.24+
.30++
.48
.71++
.79+
.41+++
.18++
.13
.07
.31+++
.12
.09
−.31
−.15
.00
−.11
.16
.00
−.02
.16
−.15
.33
.37
.46++
.27
.73+++
1.04++
.64
1.15+
.22++
.18+
.11
.25
.43+++
−.05
.10
−.88∗∗
.03
.05
−.31
−.12
−.14
−.17
.32
−.11
M
J
J
A
1.04+++ .77++
.51
.58+
.66+
.57
.19
.77+
.63+
.58+
.15
.70++
.71++
.50
.32
1.44+++
.92+++ 1.02+++ 1.42+++ 1.34
1.40+
.39
1.70
.68+
.80+
.55
.14
1.33++
1.18
.35
.34
.47
.30+++ .33+++ .23+++
.21+++
.28+++ .26++
.27+++
.25++
.08
.00
.21+++
.03
.23
.00
.46+++
.33+
+++
+++
.36
.10
.34
.28+++
−.07
.04
.14
.06
.04
.22+
.13
.20++
−.56
.18
.00
.14
−.28
.46
.46
.44
−.16
.35
.42
.27
.35
.27
.01
−.41∗
−.20
.31
.27
.05
−.49∗
.31
.10
.00
.37
.82++ −.15
−.49∗
.04
.32
.87++
.27
.00
.17
.53
.00
S
O
N
D
−.04
−.13
−.08
−.27
.57+
1.02+
−.07
.60
.20+++
.17+
.15+
.12
.11
.30+++
.22++
−.08
.77+
.56+
.68+++
.25
.02
.19
.36
.13
−.01
−.13
−.22
−.08
.30
1.09+
−.30
−.82
.12+
.29+++
.12
.17
.30+++
.12
.18
−.19
−.07
.07
.17
.18
.00
.20
.33
.00
.12
.04
.03
.19
.24+
.78++
.33
.23
.18++
.09
.14+
.24
.36+++
−.02
.32+++
.28
.03
−.01
.22++
.12
.00
−.09
.00
−.08
.18+
.03
.00
.16++
.18++
.25
.21+
.37+
.13
.27+++
.14
.23
.38+++
.10
.24+
.16
−.03
−.10
.14
−.01
−.15
−.27∗
.02
−.02
Significance is tested trough a Fisher test. Bold: signicant at the 90%. +++ , ++ , + : Positive trends significant at the 99, 95 and 90%, respectively.
∗∗∗ , ∗∗ , ∗ : Negative trends significant at the 99, 95 and 90%, respectively. Stations are ranked according to the results of HAC (Fig. 2)
weaker. In Gough and Halley, there is no significant
warming. Sub-Antarctic and mid-latitude islands stations
experience their warming all-year round in the South
Indian Ocean, although it is weaker in spring (September
to November).
The results performed on non-reconstructed temperatures over the 1958–2002 period corroborate those
from the HAC, obtained with filled values and limited
to 1973–2002. The three regions (i.e. Sub-Antarctic
and mid-latitude islands, Antarctic Peninsula and EastAntarctic) are coherent in terms of trends. Thus, the
classes obtained in Figure 2 are useful to discriminate
regions that show coherent temperature changes at the
decadal and inter-decadal time scales. For almost all stations, the Pettitt test (without any assumption on the profile of global warming) and the Fisher test (for linear fits)
converge. In the Sub-Antarctic and mid-latitude islands,
the warming is not the strongest but it is associated
with a very low interannual variability. Consequently,
the long-term linear trends are highly significant there.
We can thus conclude that a robust and early warming
occurred at the Sub-Antarctic and mid-latitude islands; in
the Antarctic Peninsula, the warming is higher and more
recent, while the East-Antarctic group shows stationary
temperatures. In regions where warming is recorded, SubAntarctic and mid-latitude islands and Antarctic Peninsula, it is strongest in summer.
East-Antarctic, Antarctic Peninsula and Sub-Antarctic
and mid-latitude islands have very distinct temperature
evolutions (both in terms of trends and interannual
variability) suggesting that temperature changes in the
southern mid- and high-latitudes have regional and seasonal characteristics that could be attributed only to
Copyright  2012 Royal Meteorological Society
hemispheric-scale mechanisms. Warming there cannot be
considered as a simple homogeneous trend in the Southern Hemisphere, but is obviously linked to more regional
modes or phenomena. The next section aims thus to link
the 24 station temperatures with atmospheric dynamics.
5.
Implication of the SAM
In this section, the 500hPa geopotential height (Z500 )
anomalies, widely considered as a good descriptor of
climate variability in the mid-latitudes (Cassou, 2008), is
used to describe atmospheric dynamics. Z500 anomalies
were derived from ERA40 and 20CR for the 1958–2002
and 1973–2002 period, over the southern mid- and
high-latitudes (south of 30 ° S). We focus here on the
austral summer season (December through February: DJF
hereafter) when largest trends occur (Table II).
We perform a principal component analyses (PCA)
applied to the 20CR (periods 1958–2002 and 1973–
2002) and ERA40 (1973–2002) 500hPa geopotential
height. The first PC respectively explains 12.1, 12.4 and
11.5% of the original variance. The variance explained
by the following PCs is not significant according to a
scree test. In Figure 4, the three PC1 clearly depict the
SAM (or Antarctic Annular Oscillation, AAO) described
by Rogers and van Loon (1982), Gong and Wang (1999)
or Thompson and Wallace (2000), among many others.
Correlation values between the Marshall SAM index and
PC1 (with an opposite sign) are respectively 0.89 (20CR
1958–2002 and 20CR 1973–2002) and 0.95 (ERA40
1973–2002). Negative (positive) loadings are associated
with positive (negative) phases of the SAM, consisting in
Int. J. Climatol. 33: 1948–1963 (2013)
1956
Y. RICHARD et al.
(a)
150
(d)
100
50
0
−50
1960 1965 1970 1975 1980 1985 1990 1995 2000
(e)
(b)
150
100
50
0
−50
1975 1980 1985 1990 1995 2000
(f)
(c)
150
100
50
0
−50
1975 1980 1985 1990 1995 2000
−0.5
0
0.5
Figure 4. First principal component of Z500 DJF anomalies. Loading pattern for: (a) 20CR 1958–2002, (b) 20CR 1973–2002 and (c) ERA40
1973–2002. Time-series and Pettitt test for (d) 20CR 1958–2002, (e) 20CR 1973–2002 and (f) ERA40 1973–2002.
a poleward (equatorward) shift and strengthening (weakening) of the mid-latitude westerly wind belt. Associated
time series (Figure 4(d)–(f)) are highly inter-correlated
(e.g. 0.95 between 20CR and ERA40 over the 1973–2002
period). Pettitt tests depict a shift between 1963 and 1964
(Figure 4(d)) followed by a regular trend towards the
positive phase (Figure 4(d)–(f)). The coherency between
results obtained over the two periods and both reanalyses
suggest reasonable robustness.
Correlation between DJF monthly values of PC1
(20CR and ERA40) with an opposite sign, Marshall
SAM index and temperature at the 24 stations are presented for both periods in Table III. To separate trends
Copyright  2012 Royal Meteorological Society
and interannual variability, correlations on detrended
series are also computed. For the East-Antarctic stations, significant values are all negative. The common
variance between the SAM index and temperature is
excessively weak (∼4% for Syowa, the most correlated station) but remains constant with detrended
series. The SAM and the East-Antarctic temperature
have a common interannual variability. Compared with
the East-Antarctic, opposite sign correlation prevails in
the Antarctic Peninsula and Sub-Antarctic and midlatitude islands. The correlations often weaken between
1958–2002 and 1973–2002 (Arturo Prat, O’Higgins,
Orcadas, Esperanza, Marambio) and, except for Gough
Int. J. Climatol. 33: 1948–1963 (2013)
1957
TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
Table III. Correlation between the SAM indexes and DJF station temperatures.
SAM\Station
1958–2002
PC1 20CR
Arturo Prat
Bellingshausen
O’Higgins
Orcadas
Faraday/Vernadsky
Rothera
Esperanza
Marambio
Amsterdam
Kerguelen
Macquarie
Crozet
Marion
Gough
Chatham
Halley
Casey
Mirny
Dumont d’Urville
Davis
Mawson
Molodeznaja
Novolazarevskaia
Syowa
1973–2002
Marshall
PC1 20CR
Raw
series
Detrended
Raw
series
Raw
series
Detrended
Raw
series
.29+++
.09
.22++
.16+
−.03
missing
.31+++
.21++
.26+++
.17+
−.07
missing
.15+
.30+++
.19++
−.05
−.17∗∗
−.18∗∗
−.12
−.15∗
−.14
−.14
−.06
−.20∗∗
.08
−.05
.15+
.06
−.16∗
missing
.21++
−.15+
.19++
.05
−.06
missing
−.03
.29+++
.16+
−.06
−.15∗
−.17∗∗
−.15∗
−.16∗
.02
−.11
−.11
−.22∗∗
.34+++
.17+
.26+++
.25+++
.09
missing
.25+++
.19+
.30+++
.19++
.02
missing
.24++
.32+++
.18++
−.15∗
−.16∗
−.20∗∗
−.13
−.16∗
−.18∗∗
−.14
−.08
−.18∗∗
.18+
.06
.20++
.17+
−.02
missing
.16+
−.05
.24+++
.09
−.16∗
missing
.10
.31+++
.15
−.16∗
−.15∗
−.19∗∗
−.16∗
−.17∗
.06
−.12
−.12
−.20∗∗
.26++
.43+++
.07
.15
.17
.35+
−.06
−.05
.13
.12
.18+
.30+
.22++
−.05
.08
.08
.07
.07
.16
.03
.06
−.05
.12
.10
Detrended
.12
.01
.05
−.01
−.15
−.22∗
.04
−.16
.14
−.05
−.00
−.04
−.10
.20+
.11
−.00
−.17
−.15
−.15
−.08
.01
−.14
−.09
−.18∗
PC1 ERA40
Raw
series
Detrended
.27++
.42+++
.13
.16
.13
.30+
−.07
−.09
.13
.06
.13
.28+
.17+
−.05
.09
.06
.01
.03
.10
−.02
.03
−.06
.09
.06
.10
−.00
.04
.03
−.15
−.23++
.04
−.13
.11
−.07
−.02
−.05
−.08
.17
.16
−.02
−.18∗
−.15
−.16
−.08
−.08
−.12
−.08
−.18∗
Marshall
Raw
series
.26++
.47+++
.15
.21++
.11
.26
−.05
−.10
.18+
.10
.15
.25
.20+
−.04
.12
.06
.04
.04
.12
−.01
.02
−.08
.07
.04
Detrended
.14
.07
.06
.01
−.07
−.14
.03
−.16
.15
−.06
−.10
.02
−.00
.19+
.08
−.10
−.20∗
−.17
−.17
−.08
.05
−.12
−.11
−.17
Significance is tested trough a Pearson test. Bold: signicant at the 90%. +++ , ++ , + : Positive correlation significant at the 99, 95 and 90%,
respectively. ∗∗∗ , ∗∗ , ∗ : Negative correlation significant at the 99, 95 and 90%, respectively. The stations are ranked according to the results of
HAC (Fig. 2)
and Amsterdam (1958–2002), become not or barely
significant after removal of the long-term trends. This
suggests that the statistical relationship between station
temperatures and the SAM is mostly due to their common trend, but does not seem (except for Gough and
Amsterdam) to hold for high-frequency time scales. This
leads us to explore the role of recurrent synoptic-scale
configurations.
6. The weather regime approach
6.1. Methodology: the k-means algorithm
Long-term warming can be linked to more regional phenomena, such as changes in the frequency of short-lived
weather regimes. In this section, we adopt thus a discretization of climate variability into recurrent configurations, or regimes (Michelangeli et al., 1995) in order
to assess to what extent such changes relate to modifications in the large-scale circulation patterns. Despite some
controversies about their existence (Stephenson et al.,
2004) and significance, as well as their number (Christiansen, 2007), it is now widely recognized that changes
in the occurrences and intrinsic properties of the weather
regimes may be an important issue for medium-range
(weekly to monthly) to climate change (decades to trend)
forecasts (Straus et al., 2007; Cassou, 2008; Pohl and
Fauchereau, 2012).
Copyright  2012 Royal Meteorological Society
Such recurrent regimes of atmospheric circulation are
obtained using the so-called k-means clustering algorithm
(Desbois et al., 1982; Michelangeli et al., 1995; Cassou,
2008). Given a preliminarily fixed number of regimes,
k, the aim of this algorithm is to obtain a partition of
the observations (days) into k regimes that minimizes
the sum of intra-regime variance. The Euclidean distance
is used to measure the similarity between two observations (days). The algorithm proceeds as follows: (1) k
random seeds are chosen as a priori centroids of the
k clusters, and (2) each day is assigned to the closest
seed according to the Euclidean distance measurement,
and new centroids are re-computed as the barycentre of
the newly formed clusters. The algorithm stops when
new iterations do not reduce intra-cluster heterogeneity
anymore.
In the present case, recurrent weather regimes are
identified through a k-means analysis of the daily
500hPa geopotential height (Z500 ) anomalies derived
from ERA40 and 20CR for the DJF 1958–2002 and
1973–2002 period, over the southern mid- and highlatitudes (south of 30 ° S). In a preliminary step, the field
is filtered by a PCA in order to reduce the dimensionality of the problem: 90% of the original variance is
retained and the k-means algorithm is applied to the subspace spanned by the first 41 PCs. The sensitivity to
the initial seeds is addressed by computing 50 different
partitions, each one being initialized by a different random draw. A classifiability index (Michelangeli et al.,
Int. J. Climatol. 33: 1948–1963 (2013)
1958
Y. RICHARD et al.
1995) is defined as the average similarity within the 50
sets of regimes: If all regimes were identical, then this
index would be 1 and indicate that final partitions are
not at all sensitive to initial seeds. The partition showing the highest similarity with the other 49 is retained.
This operation is repeated for a number of k clusters
varying between 2 and 10. Classifiability indexes are
similarly computed with 100 samples of artificial data
generated through a first-order Markov process, giving
100 classifiability values for each value of k. These
values are then sorted, their 10th and 90th percentile
values giving, respectively, the 10 and 90% confidence
limits (not shown). The classifiability index also helps
choosing the best value for k. In the present case, a
partitioning into four regimes unambiguously appears
as the best possible choice because (1) the classifiability index peaks for k = 4, indicative of lower sensitivity to initial random seeds, and (2) this value of k
is the only one for which the 90% confidence bound
is reached, showing that daily patterns of Z500 in the
Southern Hemisphere tend to converge naturally into four
well-individualized clusters. This methodology is applied
both to ERA40 and the 20CR in order to assess the
robustness of the regimes, without artefacts due to data
assimilation.
6.2. Relationships between the weather regimes
and temperature anomalies
Figure 5 shows the four weather regimes of daily Z500
anomalies defined in the 20CR over DJF 1958–2002.
Two regimes (#2 and #3) show annular patterns of opposite sign, associated with strong and spatially coherent
negative (positive) Z500 anomalies over Antarctica. They
represent, respectively, the positive and negative phases
of the SAM. The two others show clear out-of-phase
wavenumber-4 patterns (Frederiksen and Zheng, 2007)
in the mid-latitudes, indicative of synoptic-scale perturbations. Regime #1 could be modulated by the El Niño
Southern Oscillation (ENSO). Indeed, regime #1 pattern
presents similarities with those obtained from Fogt and
Bromwich (2006) who have correlated ERA40 Z500 and
the Southern Oscillation Index for DJF, and L’Heureux
and Thompson (2006) for November–March. Pohl et al.
(2010) note also that ENSO and SAM are significantly
(a) Regime #1: 1082 days
(b) Regime #2: 917 days
Gough
0.2
0.2
0.2
Orcadas
Esperanza Marambio
1.4
Esperanza Marambio
0.6
Higgins
–1.4
Bellingshausen
–1
Arturo
–0.6
0.2
0.2
Mirny
0.2
–1
Novolazarevskaia
Syowa
Molodeznaja
Kerguelen
Mawson
–1.8
Davis
Mirny
–2.2
Casey
Amsterdam
0.2
Casey
0.2
Dumont d'Urville
Dumont d'Urville
0.6
–0.2
– 0.2
0.2
(c) Regime #3: 924 days
(d) Regime #4: 1127 days
0.2
–0.2
Gough
Gough
–0.2
Marion
–0.2
–0.2
. –0.6
–0.2
Esperanza
Higgins
Arturo
Syowa
Molodeznaja
Marambio
Mawson
1.4 1.8
.
1
2.2
0.6
2.2
Davis
–0.2
Kerguelen
Molodeznaja
0.2
Amsterdam
Amsterdam
–0.2
2 Mirny
Casey
0.2
1.8
–0.2
Dumont d'Urville
–0.2
–0.2
–0.4
0.2
0.2
–0.2
−100
−50
0
50
100
Figure 5. Weather regimes of daily Z500 20CR anomalies, period DJF 1958–2002. Shadings: Composite Z500 anomalies values. Isolines: surface
temperature anomalies values. Circles: correlations between the seasonal frequency of each regime and the 24 station seasonal temperature
anomalies. Orange (blue) denotes positive (negative) temperature anomalies. For all variables, only significant values at the 95% confidence
level according to a Pearson test are shown.
Copyright  2012 Royal Meteorological Society
Int. J. Climatol. 33: 1948–1963 (2013)
1959
TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
correlated in austral summer. Regimes #2 and #3 exhibit
an out-of-phase wavenumber-4 pattern over the Southern Ocean, suggesting, as in Cash et al. (2002) and more
recently in Pohl and Fauchereau (2012), that the SAM
patterns are actually constituted of a zonally homogeneous distribution of zonally localized events (showing
well-individualized meridional structures), rather than a
zonally symmetric mode of variability per se.
Figure 5 also quantifies the daily relationship between
Z500 and 2m temperature. It shows the interannual relationships between large-scale circulation patterns and
local warming, through correlations computed between
the seasonal frequency of each regime and the seasonal
temperature anomalies at the 24 stations. Frequencies
of regimes #2 and #3 (i.e. the opposite phases of the
SAM) play an important role over the Antarctic Peninsula
and East-Antarctic temperatures, confirming Kwok and
Comiso (2002). South of 65 ° S, the positive phase of the
SAM (regime #2: Figure 5(b)) is clearly associated with
abnormally cold conditions over Antarctica. The opposite situation is observed during the negative phase of
the SAM (regime #3: Figure 5(c)). The interannual variability of this regime is associated with abnormally cold
seasonal temperature over the eight East-Antarctic stations (regime #2: Figure 5(b)). Symmetrical observations
are made for the negative phase of the SAM (regime #3:
Figure 5(c)) where the temperatures of six of the eight
stations are significantly affected. The impact of the SAM
on the Antarctic Peninsula is less clear. The 20CR and
in situ observations are not in agreement. Cape Horn is
a transition zone between anomalies of opposite signs.
In the reanalyses, the spatial resolution and the assimilation, in this complex area, may not be sufficient. North
of 60 ° S, temperature anomalies are of weak amplitude
and rarely significant. Patagonia, Amsterdam and Tasmania–New Zealand are the most impacted areas with
warm (cold) anomalies during positive (negative) phase
of the SAM. Temperature at Gough, Kerguelen and Amsterdam are positively correlated with the frequency of
the positive phase of the SAM (regime #2: Figure 5(b)).
Almost symmetrically, temperature at Gough, Kerguelen,
Amsterdam and also Marion are negatively correlated
with the frequency of its negative phase (regime #3:
Figure 5(c)).
The two last regimes include more than 50% of the
days (regime #1 and #4: Figure 5(a) and (d)). In terms
of temperature anomalies, they affect very few stations.
For instance, the temperatures in the Sub-Antarctic and
mid-latitude islands are not related to their interannual
frequency variability, except for Gough and Amsterdam
during regime #4 (Figure 5(d)).
Given the relative weakness of the relationship between
the SAM and the temperature records (Section 5;
Table III), the SAM is likely not the primary source of
spatial coherency in temperature variability in the southern Indian Ocean stations. Similarly, even if mid-latitude
transient perturbations succeed in explaining a sizeable
part of day-to-day variability, changes in their frequencies are weak at the decadal time scale and cannot alone
Copyright  2012 Royal Meteorological Society
explain the long-term warming discussed above. This
suggests that such warming relates to other mechanisms
that contribute to modify the intrinsic properties of the
regimes (e.g. their composite temperature anomalies).
7.
Relationship with SST
It seems reasonable to suppose that, for islands located
far away from any continent, the nearby SST impacts
strongly on air temperature. The linear trends in the
SST field are computed over all grid points for which
missing values concern 5 years or less over the whole
period (Figure 6). Trends are particularly high along 45 ° S
and south-eastward of South Africa, in the Agulhas Current and in the Agulhas Return Current that flows eastwards (Rouault et al., 2009). This sector is among those
where the warming is strongest (+0.5 ° C per decade, i.e.
2.2 ° C over the 1958–2002 period). This value is consistent with the temperature trends in Marion and Crozet
(Figure 3(a)). Further south along the Antarctica coast
SST tend most often to cool (Figure 6).
A similar analysis done for each month shows that
the SST warming varies over the year (Figure 7). In
the neighbouring of the Southern Indian islands, SST
warming becomes locally significant in December and
reaches maximum values in January and February. In
autumn (March to May) and in winter (June to August)
significant trends shift northward. This calendar is similar
to that obtained on air temperatures in the islands
(Table II). Warming in the Agulhas Current system
concerns a large portion of the ocean north-west of
Marion Island on the track of cyclonic low pressures
system before they reach the Marion Island (Rouault
et al., 2009, 2010). On top of the increase in the transfer
of turbulent sensible heat due to higher SST in the
region, there is also an increase in the turbulent latent
heat flux and associated transfer of moisture from the
ocean to the atmosphere (Rouault et al., 2009) associated
with warming of the Agulhas Current system. When
released through condensation, this latent energy warms
the atmosphere further.
The correlation between air temperature in each island
and nearby SST is significant (Figure 8(a)). Significant
values are spatially coherent and show clear regional patterns around the islands. Partial correlation with the Multivariate ENSO Index (MEI) (http://www.esrl.noaa.gov/
psd/enso/mei/), based on both atmospheric and oceanic
fields to consider explicitly the coupled nature of ENSO,
attests that these regional patterns are independent of
ENSO (not shown). To separate trends from interannual
variability, correlations are computed for detrended series
(Figure 8(b)). In this case, Crozet, Kerguelen and more
clearly Amsterdam display a dipolar subtropical SST feature reminiscent of the patterns previously described by
Behera and Yamagata (2001), Fauchereau et al. (2003)
and Hermes and Reason (2005), and referred to as the
Subtropical Indian Ocean Dipole (SIOD). In Marion
Island, the interannual anomalies are not associated with
Int. J. Climatol. 33: 1948–1963 (2013)
1960
Y. RICHARD et al.
60°N
30°N
0°
30°S
60°S
120°W
−0.5
−0.4
−0.3
60°W
−0.2
0°
−0.1
0
60°E
0.1
0.2
120°E
0.3
0.4
0.5
°C/10Yr
Figure 6. SST linear trends ( ° C per 10 year) according to HadSST2, period DJF 1958–2002.
SEP
OCT
NOV
DEC
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
−.6 −.4 −.2 0
.2 .4 .6
Figure 7. Monthly SST linear trends ( ° C per 10 year) according to HadSST2, period DJF 1958–2002.
regional SST. Only the strong warming trend explains
the correlation (Figure 8(a)).
8.
Discussion and conclusion
The compilation of an original database, regrouping
in situ temperature measurements for the Antarctic, the
Sub-Antarctic and mid-latitude stations, enabled us to
Copyright  2012 Royal Meteorological Society
document the similarities and differences of warming
across the Southern Ocean. Three coherent regions were
identified (namely, the East-Antarctic region, the Antarctic Peninsula and the Sub-Antarctic and mid-latitude
islands), which were shown to experience contrasted
evolutions and climate variability (see Table IV for a
summary). At the decadal and inter-decadal time scales,
warming is more pronounced in austral summer, except in
Int. J. Climatol. 33: 1948–1963 (2013)
1961
TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE
(a)
60°N
30°N
0°
30°S
Marion
60°S
Amsterdam
60°N
30°N
0°
30°S
Crozet
Kerguelen
60°S
120°W
60°W
0°
60°E
120°E
120°W
60°W
0°
60°E
120°E
(b)
60°N
30°N
0°
30°S
Marion
60°S
Amsterdam
60°N
30°N
0°
30°S
Crozet
60°S
120°W
60°W
Kerguelen
0°
60°E
120°E
−.6
−.4
−.2
R
120°W
60°W
.2
.4
0°
60°E
120°E
.6
Figure 8. Correlation between island station temperature and HadSST2, period DJF 1958–2002 (1974–2002 for Crozet), (a) with raw values,
(b) with detrended values. Only 95% significant correlations are represented.
the East-Antarctic stations where no significant warming
is recorded. Although warming is weaker at the SubAntarctic and mid-latitude islands than at the Antarctic
Peninsula, it is statistically robust because of the low temperature variability on these islands, where temperature is
strongly constrained by the surrounding ocean. In a global
warming background, trend towards the positive phase
of the SAM, which tends to cool the East-Antarctica,
explains the lack of warming in this region.
At the interannual time scale, the SAM is associated
with temperature variability in the East-Antarctic, but not
over the Antarctic Peninsula and the Sub-Antarctic and
mid-latitude islands. Its influence seems to be negligible in our northernmost stations, located in the South
Copyright  2012 Royal Meteorological Society
Table IV. Summary of the main mechanisms involved in
temperature variability.
Decadal trend
East-Antarctic Global warming −
SAM trend
Antarctic
Global warming +
Peninsula
SAM trend
Sub-Antarctic Global warming +
SAM trend
and
mid-latitude
islands
Interannual variability
SAM
?
Regional SST
(Agulhas current?)
Significance for ? : unknown.
Int. J. Climatol. 33: 1948–1963 (2013)
1962
Y. RICHARD et al.
Indian Ocean basin (Marion, Crozet, Kerguelen and Amsterdam). To explain the interannual variability and the
warming there, we show that regional SST trend is a
more likely candidate. These islands lie to the south of
the Agulhas Current system, which has intensified and
warmed by up to 1.5 ° C since the 1980s in response
to change in the Southern Hemisphere westerlies and
increase in trade winds in the Indian Ocean (Rouault
et al., 2009). Agulhas Current warming could have contributed to the warming recorded over the islands, especially during north-westerly wind conditions. However,
the recent strengthening of the Hadley circulation noted
by Mitas and Clement (2006) and Han et al. (2010) could
also be involved.
Due to the paucity of data in the region, it is not
possible to determine the cause of changes more conclusively. In this regard, hindcast simulations performed
in the framework of the fifth phase of the Coupled Model
Intercomparison Project could help refine and complete
these results.
Acknowledgements
The authors thank for the data: Météo-France, the South
African Weather Service, the British Antarctic Survey
Reader, the New Zealand National Institute of Water
and Atmospheric Research, the Australian Government
Bureau of Meteorology and the European Centre for
Medium-Range Weather Forecasts. This is a contribution to an NRF France South Africa project and to the
VOASSI programme funded by CNRS. Mathieu Rouault
thanks NRF, Nansen-Tutu Centre for Marine Environmental Research and ACCESS for funding. Calculations
were performed using HPC resources from DSI-CCUB
(Université de Bourgogne).
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