temperature changes in the mid-and high-latitudes of the southern hemisphere

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. 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-latitudesof the Southern Hemisphere

Y. Richard,a* M. Rouault,b,c B. Pohl,a J. Cretat,a I. Duclot,a S. Taboulot,d C. J. C. Reason,b

C. Macrona and D. Buirona,e

a Centre de Recherches de Climatologie, UMR6282 Biogeosciences, CNRS/universite de Bourgogne, Dijon, Franceb Department of Oceanography, Mare Institute, University of Cape Town, Rondebosch, South Africa

c Nansen-Tutu Centre for Marine Environmental Research, James Building, University of Cape Town, Cape Town, South Africad Meteo-France, Dijon, France

e Laboratoire de Glaciologie et Geophysique de l’Environnement, CNRS/Universite Joseph Fourier, Grenoble, France

ABSTRACT: A Hierarchical Ascending Classification is used to regionalize monthly temperature anomalies measuredat 24 weather stations in Antarctica and the Sub-Antarctic and mid-latitude southern islands from 1973 to 2002. Threeprincipal regions are identified that are geographically coherent: Eastern Antarctica, the Antarctic Peninsula and the Sub-Antarctic 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 temperatureanomalies are related to (1) the Southern Annular Mode (SAM) indexes computed using two reanalysis products (20thCentury Reanalyses and ERA40) over two periods (1958–2002 and 1973–2002), (2) the seasonal frequencies of fourrecurrent daily weather regimes identified with a k-means algorithm applied on the 500hPa geopotential height (DJF1958–2002) and (3) HadSST2 sea surface temperature (SST) anomalies (DJF 1958–2002). East-Antarctica interannualtemperature anomalies are associated with the SAM variability. In the Antarctic Peninsula, only the long-term trend iscommon with the SAM. The SAM does impact significantly the temperature anomalies of the Sub-Antarctic and mid-latitude islands. Trend and interannual variability of the islands’ temperatures are associated with the nearby SST. For theIndian Ocean stations, warming in the Agulhas Current system could also have led to these changes. Copyright 2012Royal Meteorological Society

KEY WORDS climate change; temperature; Southern Hemisphere; regionalization; Sub-Antarctic islands; Southern AnnularMode; 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 astronger atmospheric circumpolar flow around Antarctica,a weaker westerly flow in the mid-latitudes (Thompsonet al., 2000) and a strong oceanic warming (Gille, 2002,2008). Substantial ice mass loss inferred using radarinterferometry has occurred over the Antarctic Peninsula(Rignot et al., 2008). The recent warming recorded thereis unprecedented over the last two millennia (Vaughanet al., 2003). Gillett et al. (2008) and Monaghan andBromwich (2008) found that such observed changes inAntarctic temperature are not consistent with internal cli-mate variability or natural climate drivers alone, and aredirectly attributable to human influence. More recently,Qu et al. (2011) highlight how the anthropogenic inten-sification of global hydrological cycle induces a strongincrease of the latent heat transport into the Antarctic

* Correspondence to: Y. Richard, Centre de Recherches de Clima-tologie, CNRS/Universite de Bourgogne, 6 Bd. Gabriel, 21000 Dijon,France. E-mail: [email protected]

Peninsula, which explains the main part of the signifi-cant warming observed in this region through the 20thcentury. Polvani et al. (2011) suggest that most SouthernHemisphere tropospheric circulation changes, in australsummer 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 Sub-Antarctic region has received less attention than Antarc-tica. Yet, meteorological stations were installed in theSub-Antarctic Islands as early as the 1950s. MarionIsland climate (46.88 °S, 37.85 °E) has undergone signif-icant changes since the 1960s, mostly in austral summer(Rouault et al., 2005). They consist of a large decreasein rainfall and increases in sea level pressure, maximumand minimum local air temperature and near-shore seasurface temperature (SST). Farther east, Chapuis et al.(2004) reported that the annual mean air temperature atKerguelen Island (49.35 °S, 69.22 °E) has increased by1.3 °C since the mid-1960s. About 433 km to the south-east, at Heard Island (53.10 °S, 73.51 °E), further evidenceof climate change comes from widespread glacier retreat

Copyright 2012 Royal Meteorological Society

TEMPERATURE CHANGES IN SOUTHERN HEMISPHERE 1949

(Thost and Truffer, 2008). All these changes led to deepmodifications 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 andMcGeoch, 2008).

In spite of the importance of such modifications, expla-nations for temperature changes in the mid- and high-latitudes of the Southern Hemisphere remain incomplete.The Antarctic Peninsula and other islands are known tohave warmed up substantially in the past decades buta clear synthesis of the geography, the timing or thecharacteristics (trend or breaking) of the warming is stillmissing, not only on the Antarctic continent, but also onthe islands in the surrounding Southern Ocean. What arethe seasonal components of the warming? What are thesynoptic weather regimes associated with such tempera-ture changes?

To date, The Southern Ocean as a whole (Antarcticacoast, Sub-Antarctic and southern mid-latitude islandtime series) has not been analysed together (Gillett et al.,2006). The first aim of the present work is to documentthe spatial coherence of observed temperature changesthere. We focus on austral summer, the season that ismost sensitive to temperature changes. The second aimis to quantify to what extent changes noted in observedin situ air temperatures relate to changes in synoptic-scaleweather regime occurrences and/or trends in SST andSouthern Annular Mode (SAM).

2. Data

2.1. The ERA40 and 20th Century Reanalyses

The European Centre for Medium-Range Weather Fore-casts (ECMWF) ERA40 reanalysis (Uppala et al., 2005),available from 1958 to 2002, was used to document thelarge-scale circulation and atmospheric configurations.Marshall (2003) and Bromwich and Fogt (2004) com-pared ERA40 and NCEP/NCAR reanalysis with Antarc-tic and other mid- to high-latitude station observationsfrom 1958 to 2001. They found that ERA40, a second-generation reanalysis that assimilated many Antarctic sta-tions from the start of the run, was generally more inagreement with in situ observations. Prior to the late1970s, the quality of the reanalyses depends on the sea-son: Bromwich et al. (2007) indicate that they are onlyreliable during summer.

To ensure the robustness of the results and take intoaccount the uncertainties identified by Bromwich andFogt (2004): ‘a more detailed look at the presatelliteera reveals many shortcomings in ERA40, particularlyin the Southern winter’, the same tests were also con-ducted with the 20th Century Reanalysis version 2 (20CRhereafter), a dataset with time-consistent data assimila-tion (Compo et al., 2011), recently used to documentdecadal changes in the region (Pohl and Fauchereau,2012). 20CR are available since 1871 on a 2° × 2° reg-ular grid, and assimilate only surface data through an

ensemble Kalman filter, ensuring consistency between thepre-satellite and the satellite era. 20CR fields used hereare the ensemble mean of 56 members. Although surfacedata were assimilated since the first year of the reanal-ysis in the southern mid-latitudes, the first data assimi-lated in Antarctica date back from the early 1910s, witha 30 year gap between the two World Wars. Althoughin situ observed data remain rare in the Southern Hemi-sphere even in recent years, the station network is denserand almost constant since the International Geophysi-cal Year (1957) only. Over the Southern Ocean, a spe-cific issue concerns the non-consideration of the inter-annual sea-ice extent variations. Reanalyses are morereliable in summer when the jet stream does not shiftsignificantly regardless of whether the sea-ice edge isextended or contracted (Kidston et al., 2011). In win-ter, their reliability increased during the satellite era(even for the 20CR), due to better monitoring of sea-iceextent. In this work, the 20CR are therefore used overthe 1958–2002 period, focusing on the austral summerseason.

2.2. The HadSST2 dataset

The UK Meteorological Office Hadley Centre’s SSTdataset version 2 (HadSST2) is a monthly global SSTdataset provided on a 5° × 5° regular grid since 1850(Rayner et al., 2006). This dataset is only based onobservations and does not interpolate surface temperatureover the regions that could not be documented. It is thusthought to minimize statistical artefacts in the southernhigh-latitudes, where in situ measurements are rare.

2.3. An original and international temperature dataset

Provided by numerous weather services (Meteo-France,the South African Weather Service, the British Antarc-tic Survey Reader, the New Zealand National Instituteof Water and Atmospheric Research and the AustralianGovernment Bureau of Meteorology), 24 monthly tem-perature time series were compiled for this study (Table I,Figure 1). They document the mid- to high-latitude tem-peratures of the Southern Ocean, except over the SouthPacific where there is no measurement. The dates fromwhich data are available vary from 1950 to 1974 andmost of these series include missing values (from 0 to11.6%, and 2.9% on average, between their opening dateand 2002: see Table I).

2.4. The Marshall SAM index

To compare our results with a recognized index ofthe SAM, we consider the Marshall monthly index(http://www.antarctica.ac.uk/met/gjma/sam.html) docu-menting the meridional pressure gradient between 40 °Sand 65 °S. This index was computed with observed sealevel pressure at 12 stations using the methodology ofMarshall (2003).

Copyright 2012 Royal Meteorological Society Int. J. Climatol. 33: 1948–1963 (2013)

1950 Y. RICHARD et al.

Table I. Summary of the monthly temperature records.

Meteorologicalstation name

Antarctica situationor Island (Is.)

Area orocean

Country ofmeteorological

survey

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Altitude(m)

Startdate

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Amsterdam Amsterdam Is. Indian Ocean France 37.8 77.5 E 27 1950 0.4Arturo Prat South Shetland Is. Ant. Peninsula Chile 62.5 59.7 W 5 1960 0.0Bellingshausen King George Is. Ant. Peninsula Russia 62.2 58.9 W 16 1968 0.4Casey Vincennes Bay East Antarctic Australia 66.3 110.5 E 42 1957 0.6Chatham Chatham Is. Pacific Ocean New Zealand 44.0 176.6 E 44 1956 11.6Crozet Crozet Is. Indian Ocean France 46.4 51.9 E 146 1974 4.4Davis Princess Eliz. Land East Antarctic Australia 68.6 78.0 E 13 1957 9.0Dumont d’Urville Adelie Land East Antarctic France 66.7 140.0 E 43 1956 0.2Esperanza Hope Bay Ant. Peninsula Argentina 63.4 57.0 W 13 1960 4.9Faraday/Vernadsky Galindez Is. Ant. Peninsula UK/Ukrania 65.4 64.4 W 11 1950 3.5Gough Tristan da Cunha Is. Atlantic Ocean South Africa 40.4 9.9 W 54 1956 0.5Halley Halley Bay West Antarctic UK 75.6 26.6 W 30 1957 0.0Kerguelen Kerguelen Is. Indian Ocean France 49.3 70.2 E 29 1951 0.0Macquarie Tmacquarie Is. Pacific Ocean Australia 54.5 158.9 E 8 1948 5.2Marambio Marambio Is. Weddel Sea Argentina 64.2 56.7 W 198 1970 4.9Marion Prince Edward Is. Indian Ocean South Africa 46.8 37.8 E 24 1951 2.9Mawson Mc Robertson Land East Antarctic Australia 67.6 62.9 E 16 1954 0.8Mirny Davis Sea East Antarctic Russia 66.5 93.0 E 30 1956 0.5Molodeznaja Cosmonaut Sea East Antarctic Russia 67.7 45.9 E 40 1963 1.6Novolazarevskaia Queen Maud Land East Antarctic Russia 70.8 11.8 E 119 1961 0.5O’Higgins North end Peninsula Ant. Peninsula Chile 63.3 57.9 W 10 1963 2.2Orcadas Orcadas Is. Ant. Peninsula Argentina 60.7 44.7 W 6 1950 4.0Rothera Adelaide Is. Ant. Peninsula UK 67.5 68.1 W 16 1977 1.0Syowa East Ongul Is. East Antarctic Japan 69.0 39.6 E 21 1957 10.3

Figure 1. Observed in situ data. Shaded areas correspond to the Hierarchical Ascending Classification results.



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Figure 2. Clustering tree of the Hierarchical Ascending Classification applied to normalized monthly temperature anomalies.

3. Regionalization of monthly temperatureanomalies

3.1. Methodology: the Hierarchical AscendingClassification

We attempt first to regionalize the temperature evolu-tions. A Hierarchical Ascending Classification (HAC)algorithm is applied to the 24 monthly series to iden-tify which stations can be objectively grouped togetheraccording to their similarities. To exclude the seasonal-ity, the classification is applied over monthly standardizedtemperature anomalies. The ascending nature of the HACtechnique means that, at the start of the algorithm, eachstation constitutes a separate class (Figure 2). Then, iter-atively, 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. Thecriterion used to aggregate the most similar classes isWard’s method (Ward, 1963), based on Euclidean dis-tances, and also called minimum variance clustering. Itis based on the minimization of the intra-class inertia(i.e. it minimizes the heterogeneity between stations of agiven class).

The HAC does not support missing values. Before(since) 1973, missing values are frequent (sparse) formost of in situ temperature series. Usual methods ofrecovery data (e.g. regression) are often based on neigh-bouring series. In our study, the stations are often very farfrom each other, which make this approach inappropri-ate. Hence, an alternative method is applied, consistingto relate observed values to large-scale atmospheric pat-terns. We filled monthly temperature gaps through simplelinear regressions with ERA40 temperature at the nearestgrid point. Following Bromwich and Fogt (2004), whonoticed abrupt shifts in the 1960s due to data assimila-tion inconstancies, we restricted our data reconstruction(and thus our HAC analysis) to the period 1973–2002.

3.2. Spatialization of temperature evolutions

One cluster highlights a specific signal to the Antarc-tic Peninsula and neighbouring islands (Figure 2). Theassociated 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 monthlytemperature anomalies (Figure 2). The 16 remaining sta-tions are discretized into two well-separated regions, the‘East-Antarctic’ region (Casey, Mirny, Dumont d’Urville,Davis, Mawson, Molodeznaja, Novolazarevskaia andSyowa) and the ‘Sub-Antarctic and mid-latitude islands’(Amsterdam, Kerguelen, Macquarie, Crozet, Marion,Gough, Chatham), to which must be added Halley, theonly ‘West-Antarctic’ station. It is located on the BruntIce Shelf floating on the Weddell Sea, and experiences acoastal ice shelf climate, isolated by the HAC when atleast four classes are considered. It has previously beenidentified as experiencing a different climate from that ofthe Antarctic Peninsula (Turner et al., 2005; Steig et al.,2009).

Some results were expected, e.g. the consistency ofthe Antarctic Peninsula and the clear predominanceof geographical proximity in the determination of theclasses. These results nonetheless reflect the existenceof specific regional characteristics and give confidencein the reliability of the data and the usefulness of theHAC. Others results are novel, particularly the Sub-Antarctic and mid-latitude islands coherency, despite thefact that these islands are separated by several thousandsof kilometres. For these stations a latitudinal logicprevails. However, given that the rare (<5%) missingvalues were filled from ERA40, this could contributeto exaggerate the similarities between stations. Thefollowing analyses will therefore use only data withoutreconstruction, in order to assess the robustness of theclassification and to detail the characteristics of eachcluster.



4. Analysis of temperature trends

To test and detail the results of the HAC (Section 3), weperform an analysis for each station, on a longer period(1958–2002), and without data reconstitutions. Trends atthe 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 (bro-ken curves) are identified using a nonparametric Pettitttest, which is based on the Mann–Whitney test (Pettitt,1979). In the Sub-Antarctic and mid-latitude islands

(Figure 3(a)), the Pettitt test detects non-stationary tem-peratures for six stations: the four Indian stations (Mar-ion, Crozet, Kerguelen and Amsterdam) and the twoPacific islands (Macquarie and Chatham). This signaltakes the form of linear warming trends, without suddenruptures, as shown by the parabolic curves of the Pettitttest. Their extrema indicate that the warming is partic-ularly early (1960s and 1970s), especially at Macquarieand Amsterdam, and strong in the South Indian Oceanislands (Marion, Kerguelen and Amsterdam). The warm-ing trends remain even significant in the two shorter or

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incomplete series (Crozet and Chatham). Gough Island,the only record in the Atlantic sector, presents a smallshift (sudden warming near 1973) but no significant trendoverall. There is no significant temperature change inHalley (i.e. the West-Antarctic station). Over the Antarc-tic Peninsula (Figure 3(b)), warming trends are signifi-cant at almost all stations. Although differences betweenthe time period covered by the eight series do not allowus to describe with confidence the common trend, itseems that warming starts later over Antarctic Peninsula(maximum in the 1980s) than in the Sub-Antarctic and

mid-latitude islands. No station of the Antarctic Peninsulareaches statistical significance (according to the Pettitttest) 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 sevenof eight stations do not experience any significant change.Only Novolazarevskaia, the westernmost station, experi-enced a significant warming.

To complete these analyses, linear adjustments arecomputed to quantify the annual and monthly warm-ing (Table II). Annual mean temperature exhibits linear



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warming trends in all stations of the Antarctic Penin-sula region. The trends range from +0.22 (O’Higgins)to +0.72 °C per 10 year (Rothera). Warming dominatesfrom January to August, in agreement with previousresults (Jacka and Budd, 1998; Steig et al., 2009; Quet al., 2011). It is hardly perceptible in spring (fromSeptember to December), confirming once again Jackaand Budd (1998). In contrast, among the East-Antarcticregion, six stations do not show significant tempera-ture trends. Only Novolazarevskaya and Casey havetrends significant at the 99 and 90% level, respectively.

The distinct seasonal trends identified by Schneideret al. (2011), with some cooling in summer and autumncontrasting with warming in winter and spring, areoften non-significant. The isolated warming of July(when the weather makes measurements difficult) inNovolazarevskaia (as in Molodeznaja) raises the questionof data reliability. In the Sub-Antarctic and mid-latitudeislands region, the warming ranks from +0.20 per 10 year(Amsterdam) to +0.28 °C per 10 year (Marion). Farthereast, south of Tasmania (Macquarie) and in the PacificOcean (Chatham), warming remains significant but is



Table II. Linear annual and monthly temperature trends ( °C/10yr).

Station\Date Yr J F M A M J J A S O N D

Arturo Prat .33+++ .35+++ .45+++ .44+++ .33 1.04+++ .77++ .51 .58+ −.04 −.01 .12 .18+Bellingshausen .23+++ .36+++ .28++ .26+ .37 .66+ .57 .19 .77+ −.13 −.13 .04 .03O’Higgins .22+++ .09 .28+++ .25+ .46++ .63+ .58+ .15 .70++ −.08 −.22 .03 .00Orcadas .23+++ .26+++ .29+++ .24+ .27 .71++ .50 .32 1.44+++ −.27 −.08 .19 .16++Faraday/Vernadsky .53+++ .29+++ .30+++ .30++ .73+++ .92+++ 1.02+++ 1.42+++ 1.34 .57+ .30 .24+ .18++Rothera .72+++ .36++ .50+++ .48 1.04++ 1.40+ .39 1.70 .68+ 1.02+ 1.09+ .78++ .25Esperanza .32+++ .33++ .75+++ .71++ .64 .80+ .55 .14 1.33++ −.07 −.30 .33 .21+Marambio .48+++ .66+++ .36++ .79+ 1.15+ 1.18 .35 .34 .47 .60 −.82 .23 .37+Amsterdam .20+++ .30+++ .31+++ .41+++ .22++ .30+++ .33+++ .23+++ .21+++ .20+++ .12+ .18++ .13Kerguelen .21+++ .37+++ .32+++ .18++ .18+ .28+++ .26++ .27+++ .25++ .17+ .29+++ .09 .27+++Macquarie .11+++ .19++ .20++ .13 .11 .08 .00 .21+++ .03 .15+ .12 .14+ .14Crozet .21+++ .54+++ .41++ .07 .25 .23 .00 .46+++ .33+ .12 .17 .24 .23Marion .28+++ .44+++ .42+++ .31+++ .43+++ .36+++ .10 .34+++ .28+++ .11 .30+++ .36+++ .38+++Gough .03 .09 .00 .12 −.05 −.07 .04 .14 .06 .30+++ .12 −.02 .10Chatham .15+++ .29++ .18 .09 .10 .04 .22+ .13 .20++ .22++ .18 .32+++ .24+Halley −.09 .13 −.11 −.31 −.88∗∗ −.56 .18 .00 .14 −.08 −.19 .28 .16Casey .10+ −.10 −.07 −.15 .03 −.28 .46 .46 .44 .77+ −.07 .03 −.03Mirny .08 −.19 −.04 .00 .05 −.16 .35 .42 .27 .56+ .07 −.01 −.10Dumont d’Urville .06 .00 .08 −.11 −.31 −.41∗ .35 .27 .01 .68+++ .17 .22++ .14Davis .08 .05 .09 .16 −.12 −.20 .31 .27 .05 .25 .18 .12 −.01Mawson −.03 −.07 −.04 .00 −.14 −.49∗ .31 .10 .00 .02 .00 .00 −.15Molodeznaja −.01 −.25 .06 −.02 −.17 −.49∗ .37 .82++ −.15 .19 .20 −.09 −.27∗Novolazarevskaia .21+++ .17 .15 .16 .32 .04 .32 .87++ .27 .36 .33 .00 .02Syowa .04 −.07 .16 −.15 −.11 .00 .17 .53 .00 .13 .00 −.08 −.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 significantwarming. Sub-Antarctic and mid-latitude islands stationsexperience their warming all-year round in the SouthIndian Ocean, although it is weaker in spring (Septemberto November).

The results performed on non-reconstructed temper-atures over the 1958–2002 period corroborate thosefrom the HAC, obtained with filled values and limitedto 1973–2002. The three regions (i.e. Sub-Antarcticand mid-latitude islands, Antarctic Peninsula and East-Antarctic) are coherent in terms of trends. Thus, theclasses obtained in Figure 2 are useful to discriminateregions that show coherent temperature changes at thedecadal and inter-decadal time scales. For almost all sta-tions, the Pettitt test (without any assumption on the pro-file 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 associatedwith a very low interannual variability. Consequently,the long-term linear trends are highly significant there.We can thus conclude that a robust and early warmingoccurred at the Sub-Antarctic and mid-latitude islands; inthe Antarctic Peninsula, the warming is higher and morerecent, while the East-Antarctic group shows stationarytemperatures. In regions where warming is recorded, Sub-Antarctic and mid-latitude islands and Antarctic Penin-sula, it is strongest in summer.

East-Antarctic, Antarctic Peninsula and Sub-Antarcticand mid-latitude islands have very distinct temperatureevolutions (both in terms of trends and interannualvariability) suggesting that temperature changes in thesouthern mid- and high-latitudes have regional and sea-sonal characteristics that could be attributed only to

hemispheric-scale mechanisms. Warming there cannot beconsidered as a simple homogeneous trend in the South-ern Hemisphere, but is obviously linked to more regionalmodes or phenomena. The next section aims thus to linkthe 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 ofclimate variability in the mid-latitudes (Cassou, 2008), isused to describe atmospheric dynamics. Z500 anomalieswere derived from ERA40 and 20CR for the 1958–2002and 1973–2002 period, over the southern mid- andhigh-latitudes (south of 30 °S). We focus here on theaustral summer season (December through February: DJFhereafter) 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 geopotentialheight. The first PC respectively explains 12.1, 12.4 and11.5% of the original variance. The variance explainedby the following PCs is not significant according to ascree test. In Figure 4, the three PC1 clearly depict theSAM (or Antarctic Annular Oscillation, AAO) describedby Rogers and van Loon (1982), Gong and Wang (1999)or Thompson and Wallace (2000), among many others.Correlation values between the Marshall SAM index andPC1 (with an opposite sign) are respectively 0.89 (20CR1958–2002 and 20CR 1973–2002) and 0.95 (ERA401973–2002). Negative (positive) loadings are associatedwith positive (negative) phases of the SAM, consisting in



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Figure 4. First principal component of Z500 DJF anomalies. Loading pattern for: (a) 20CR 1958–2002, (b) 20CR 1973–2002 and (c) ERA401973–2002. 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 (weak-ening) of the mid-latitude westerly wind belt. Associatedtime series (Figure 4(d)–(f)) are highly inter-correlated(e.g. 0.95 between 20CR and ERA40 over the 1973–2002period). Pettitt tests depict a shift between 1963 and 1964(Figure 4(d)) followed by a regular trend towards thepositive phase (Figure 4(d)–(f)). The coherency betweenresults obtained over the two periods and both reanalysessuggest reasonable robustness.

Correlation between DJF monthly values of PC1(20CR and ERA40) with an opposite sign, MarshallSAM index and temperature at the 24 stations are pre-sented for both periods in Table III. To separate trends

and interannual variability, correlations on detrendedseries are also computed. For the East-Antarctic sta-tions, significant values are all negative. The commonvariance between the SAM index and temperature isexcessively weak (∼4% for Syowa, the most cor-related station) but remains constant with detrendedseries. The SAM and the East-Antarctic temperaturehave a common interannual variability. Compared withthe East-Antarctic, opposite sign correlation prevails inthe Antarctic Peninsula and Sub-Antarctic and mid-latitude islands. The correlations often weaken between1958–2002 and 1973–2002 (Arturo Prat, O’Higgins,Orcadas, Esperanza, Marambio) and, except for Gough



Table III. Correlation between the SAM indexes and DJF station temperatures.

SAM\Station 1958–2002 1973–2002

PC1 20CR Marshall PC1 20CR PC1 ERA40 Marshall

Rawseries

DetrendedRawseries

Rawseries

Detrended Rawseries

Detrended Rawseries

Detrended Rawseries

Detrended

Arturo Prat .29+++ .08 .34+++ .18+ .26++ .12 .27++ .10 .26++ .14Bellingshausen .09 −.05 .17+ .06 .43+++ .01 .42+++ −.00 .47+++ .07O’Higgins .22++ .15+ .26+++ .20++ .07 .05 .13 .04 .15 .06Orcadas .16+ .06 .25+++ .17+ .15 −.01 .16 .03 .21++ .01Faraday/Vernadsky −.03 −.16∗ .09 −.02 .17 −.15 .13 −.15 .11 −.07Rothera missing missing missing missing .35+ −.22∗ .30+ −.23++ .26 −.14Esperanza .31+++ .21++ .25+++ .16+ −.06 .04 −.07 .04 −.05 .03Marambio .21++ −.15+ .19+ −.05 −.05 −.16 −.09 −.13 −.10 −.16Amsterdam .26+++ .19++ .30+++ .24+++ .13 .14 .13 .11 .18+ .15Kerguelen .17+ .05 .19++ .09 .12 −.05 .06 −.07 .10 −.06Macquarie −.07 −.06 .02 −.16∗ .18+ −.00 .13 −.02 .15 −.10Crozet missing missing missing missing .30+ −.04 .28+ −.05 .25 .02Marion .15+ −.03 .24++ .10 .22++ −.10 .17+ −.08 .20+ −.00Gough .30+++ .29+++ .32+++ .31+++ −.05 .20+ −.05 .17 −.04 .19+Chatham .19++ .16+ .18++ .15 .08 .11 .09 .16 .12 .08Halley −.05 −.06 −.15∗ −.16∗ .08 −.00 .06 −.02 .06 −.10Casey −.17∗∗ −.15∗ −.16∗ −.15∗ .07 −.17 .01 −.18∗ .04 −.20∗Mirny −.18∗∗ −.17∗∗ −.20∗∗ −.19∗∗ .07 −.15 .03 −.15 .04 −.17Dumont d’Urville −.12 −.15∗ −.13 −.16∗ .16 −.15 .10 −.16 .12 −.17Davis −.15∗ −.16∗ −.16∗ −.17∗ .03 −.08 −.02 −.08 −.01 −.08Mawson −.14 .02 −.18∗∗ .06 .06 .01 .03 −.08 .02 .05Molodeznaja −.14 −.11 −.14 −.12 −.05 −.14 −.06 −.12 −.08 −.12Novolazarevskaia −.06 −.11 −.08 −.12 .12 −.09 .09 −.08 .07 −.11Syowa −.20∗∗ −.22∗∗ −.18∗∗ −.20∗∗ .10 −.18∗ .06 −.18∗ .04 −.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 ofHAC (Fig. 2)

and Amsterdam (1958–2002), become not or barelysignificant after removal of the long-term trends. Thissuggests that the statistical relationship between stationtemperatures and the SAM is mostly due to their com-mon trend, but does not seem (except for Gough andAmsterdam) to hold for high-frequency time scales. Thisleads us to explore the role of recurrent synoptic-scaleconfigurations.

6. The weather regime approach

6.1. Methodology: the k-means algorithm

Long-term warming can be linked to more regional phe-nomena, such as changes in the frequency of short-livedweather regimes. In this section, we adopt thus a dis-cretization of climate variability into recurrent configu-rations, or regimes (Michelangeli et al., 1995) in orderto assess to what extent such changes relate to modifica-tions in the large-scale circulation patterns. Despite somecontroversies about their existence (Stephenson et al.,2004) and significance, as well as their number (Chris-tiansen, 2007), it is now widely recognized that changesin the occurrences and intrinsic properties of the weatherregimes 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 andFauchereau, 2012).

Such recurrent regimes of atmospheric circulation areobtained 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 ofthe observations (days) into k regimes that minimizesthe sum of intra-regime variance. The Euclidean distanceis used to measure the similarity between two observa-tions (days). The algorithm proceeds as follows: (1) k

random seeds are chosen as a priori centroids of thek clusters, and (2) each day is assigned to the closestseed according to the Euclidean distance measurement,and new centroids are re-computed as the barycentre ofthe newly formed clusters. The algorithm stops whennew iterations do not reduce intra-cluster heterogeneityanymore.

In the present case, recurrent weather regimes areidentified through a k-means analysis of the daily500hPa geopotential height (Z500) anomalies derivedfrom ERA40 and 20CR for the DJF 1958–2002 and1973–2002 period, over the southern mid- and high-latitudes (south of 30 °S). In a preliminary step, the fieldis filtered by a PCA in order to reduce the dimension-ality of the problem: 90% of the original variance isretained and the k-means algorithm is applied to the sub-space spanned by the first 41 PCs. The sensitivity tothe initial seeds is addressed by computing 50 differentpartitions, each one being initialized by a different ran-dom draw. A classifiability index (Michelangeli et al.,



1995) is defined as the average similarity within the 50sets of regimes: If all regimes were identical, then thisindex would be 1 and indicate that final partitions arenot at all sensitive to initial seeds. The partition show-ing the highest similarity with the other 49 is retained.This operation is repeated for a number of k clustersvarying between 2 and 10. Classifiability indexes aresimilarly computed with 100 samples of artificial datagenerated through a first-order Markov process, giving100 classifiability values for each value of k. Thesevalues are then sorted, their 10th and 90th percentilevalues giving, respectively, the 10 and 90% confidencelimits (not shown). The classifiability index also helpschoosing the best value for k. In the present case, apartitioning into four regimes unambiguously appearsas the best possible choice because (1) the classifiabil-ity index peaks for k = 4, indicative of lower sensi-tivity to initial random seeds, and (2) this value of k

is the only one for which the 90% confidence boundis reached, showing that daily patterns of Z500 in theSouthern Hemisphere tend to converge naturally into fourwell-individualized clusters. This methodology is applied

both to ERA40 and the 20CR in order to assess therobustness of the regimes, without artefacts due to dataassimilation.

6.2. Relationships between the weather regimesand 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 oppo-site sign, associated with strong and spatially coherentnegative (positive) Z500 anomalies over Antarctica. Theyrepresent, respectively, the positive and negative phasesof the SAM. The two others show clear out-of-phasewavenumber-4 patterns (Frederiksen and Zheng, 2007)in the mid-latitudes, indicative of synoptic-scale pertur-bations. Regime #1 could be modulated by the El NinoSouthern Oscillation (ENSO). Indeed, regime #1 patternpresents similarities with those obtained from Fogt andBromwich (2006) who have correlated ERA40 Z500 andthe Southern Oscillation Index for DJF, and L’Heureuxand Thompson (2006) for November–March. Pohl et al.(2010) note also that ENSO and SAM are significantly

.

.2

–0.2

–0.2 –0.4

0.2

–0.2

–0.2

–0.2

2.2

1.81.4

10.6

2.2

1.8

1.4

0.2

–1

0.2

0.2

0.2

0.6

0.2

–0.2

0.6

0.2

0.2

0.2–1.8

–2.2

–1

– 0.2

–1.4

–0.6

–0.2

–0.2

–0.6

0.20.2

0.2

0.2

0.2

–0.2–0.2

–0.2

–0.2

−100 −50 0 50 100

(c) Regime #3: 924 days (d) Regime #4: 1127 days

(a) Regime #1: 1082 days (b) Regime #2: 917 days

Dumont d'Urville

Casey

Mirny

Esperanza

Dumont d'Urville

Casey

Mirny

SyowaMolodeznaja

Mawson

Davis

Dumont d'Urville

Casey

Mirny

SyowaMolodeznaja

Mawson

Davis

Kerguelen

Amsterdam

Gough

Molodeznaja

Gough

MarambioEsperanza

Novolazarevskaia

Kerguelen

Amsterdam

Marambio

Higgins

Orcadas

ArturoBellingshausen

Marion

EsperanzaMarambio

HigginsArturo

Amsterdam

Gough

Figure 5. Weather regimes of daily Z500 20CR anomalies, period DJF 1958–2002. Shadings: Composite Z500 anomalies values. Isolines: surfacetemperature anomalies values. Circles: correlations between the seasonal frequency of each regime and the 24 station seasonal temperatureanomalies. 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.



correlated in austral summer. Regimes #2 and #3 exhibitan out-of-phase wavenumber-4 pattern over the South-ern Ocean, suggesting, as in Cash et al. (2002) and morerecently in Pohl and Fauchereau (2012), that the SAMpatterns are actually constituted of a zonally homoge-neous distribution of zonally localized events (showingwell-individualized meridional structures), rather than azonally symmetric mode of variability per se.

Figure 5 also quantifies the daily relationship betweenZ500 and 2m temperature. It shows the interannual rela-tionships between large-scale circulation patterns andlocal warming, through correlations computed betweenthe seasonal frequency of each regime and the seasonaltemperature anomalies at the 24 stations. Frequenciesof regimes #2 and #3 (i.e. the opposite phases of theSAM) play an important role over the Antarctic Peninsulaand East-Antarctic temperatures, confirming Kwok andComiso (2002). South of 65 °S, the positive phase of theSAM (regime #2: Figure 5(b)) is clearly associated withabnormally cold conditions over Antarctica. The oppo-site situation is observed during the negative phase ofthe SAM (regime #3: Figure 5(c)). The interannual vari-ability of this regime is associated with abnormally coldseasonal temperature over the eight East-Antarctic sta-tions (regime #2: Figure 5(b)). Symmetrical observationsare made for the negative phase of the SAM (regime #3:Figure 5(c)) where the temperatures of six of the eightstations are significantly affected. The impact of the SAMon the Antarctic Peninsula is less clear. The 20CR andin situ observations are not in agreement. Cape Horn isa transition zone between anomalies of opposite signs.In the reanalyses, the spatial resolution and the assimila-tion, in this complex area, may not be sufficient. Northof 60 °S, temperature anomalies are of weak amplitudeand rarely significant. Patagonia, Amsterdam and Tas-mania–New Zealand are the most impacted areas withwarm (cold) anomalies during positive (negative) phaseof the SAM. Temperature at Gough, Kerguelen and Ams-terdam are positively correlated with the frequency ofthe positive phase of the SAM (regime #2: Figure 5(b)).Almost symmetrically, temperature at Gough, Kerguelen,Amsterdam and also Marion are negatively correlatedwith the frequency of its negative phase (regime #3:Figure 5(c)).

The two last regimes include more than 50% of thedays (regime #1 and #4: Figure 5(a) and (d)). In termsof temperature anomalies, they affect very few stations.For instance, the temperatures in the Sub-Antarctic andmid-latitude islands are not related to their interannualfrequency variability, except for Gough and Amsterdamduring regime #4 (Figure 5(d)).

Given the relative weakness of the relationship betweenthe SAM and the temperature records (Section 5;Table III), the SAM is likely not the primary source ofspatial coherency in temperature variability in the south-ern Indian Ocean stations. Similarly, even if mid-latitudetransient perturbations succeed in explaining a sizeablepart of day-to-day variability, changes in their frequen-cies are weak at the decadal time scale and cannot alone

explain the long-term warming discussed above. Thissuggests that such warming relates to other mechanismsthat contribute to modify the intrinsic properties of theregimes (e.g. their composite temperature anomalies).

7. Relationship with SST

It seems reasonable to suppose that, for islands locatedfar away from any continent, the nearby SST impactsstrongly on air temperature. The linear trends in theSST field are computed over all grid points for whichmissing values concern 5 years or less over the wholeperiod (Figure 6). Trends are particularly high along 45 °Sand south-eastward of South Africa, in the Agulhas Cur-rent and in the Agulhas Return Current that flows east-wards (Rouault et al., 2009). This sector is among thosewhere the warming is strongest (+0.5 °C per decade, i.e.2.2 °C over the 1958–2002 period). This value is con-sistent with the temperature trends in Marion and Crozet(Figure 3(a)). Further south along the Antarctica coastSST tend most often to cool (Figure 6).

A similar analysis done for each month shows thatthe SST warming varies over the year (Figure 7). Inthe neighbouring of the Southern Indian islands, SSTwarming becomes locally significant in December andreaches maximum values in January and February. Inautumn (March to May) and in winter (June to August)significant trends shift northward. This calendar is similarto that obtained on air temperatures in the islands(Table II). Warming in the Agulhas Current systemconcerns a large portion of the ocean north-west ofMarion Island on the track of cyclonic low pressuressystem before they reach the Marion Island (Rouaultet al., 2009, 2010). On top of the increase in the transferof turbulent sensible heat due to higher SST in theregion, there is also an increase in the turbulent latentheat flux and associated transfer of moisture from theocean to the atmosphere (Rouault et al., 2009) associatedwith warming of the Agulhas Current system. Whenreleased through condensation, this latent energy warmsthe atmosphere further.

The correlation between air temperature in each islandand nearby SST is significant (Figure 8(a)). Significantvalues are spatially coherent and show clear regional pat-terns around the islands. Partial correlation with the Mul-tivariate ENSO Index (MEI) (http://www.esrl.noaa.gov/psd/enso/mei/), based on both atmospheric and oceanicfields to consider explicitly the coupled nature of ENSO,attests that these regional patterns are independent ofENSO (not shown). To separate trends from interannualvariability, correlations are computed for detrended series(Figure 8(b)). In this case, Crozet, Kerguelen and moreclearly Amsterdam display a dipolar subtropical SST fea-ture reminiscent of the patterns previously described byBehera and Yamagata (2001), Fauchereau et al. (2003)and Hermes and Reason (2005), and referred to as theSubtropical Indian Ocean Dipole (SIOD). In MarionIsland, the interannual anomalies are not associated with



−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5

°C/10Yr

60°S

30°S

0°

30°N

60°N

120°W 60°W 0° 60°E 120°E

Figure 6. SST linear trends ( °C per 10 year) according to HadSST2, period DJF 1958–2002.

SEP OCT NOV

JAN FEBDEC

MAR APR MAY

JUN JUL AUG

−.6 −.4 −.2 .2 .4 .60

Figure 7. Monthly SST linear trends ( °C per 10 year) according to HadSST2, period DJF 1958–2002.

regional SST. Only the strong warming trend explainsthe correlation (Figure 8(a)).

8. Discussion and conclusion

The compilation of an original database, regroupingin situ temperature measurements for the Antarctic, theSub-Antarctic and mid-latitude stations, enabled us to

document the similarities and differences of warmingacross the Southern Ocean. Three coherent regions wereidentified (namely, the East-Antarctic region, the Antarc-tic Peninsula and the Sub-Antarctic and mid-latitudeislands), which were shown to experience contrastedevolutions and climate variability (see Table IV for asummary). At the decadal and inter-decadal time scales,warming is more pronounced in austral summer, except in



Marion

KerguelenCrozet

Amsterdam

(b)

60°S

30°S

0°

30°N

60°N

60°S

30°S

0°

30°N

60°N

−.6 −.4 −.2 .2 .4 .6R

Marion

KerguelenCrozet

Amsterdam

(a)

60°S

30°S

0°

30°N

60°N

60°S

30°S

0°

30°N

60°N

120°W 60°W 0° 60°E 120°E 120°W 60°W 0° 60°E 120°E

120°W 60°W 0° 60°E 120°E 120°W 60°W 0° 60°E 120°E

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 warmingis recorded. Although warming is weaker at the Sub-Antarctic and mid-latitude islands than at the AntarcticPeninsula, it is statistically robust because of the low tem-perature variability on these islands, where temperature isstrongly constrained by the surrounding ocean. In a globalwarming background, trend towards the positive phaseof 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 associatedwith temperature variability in the East-Antarctic, but notover the Antarctic Peninsula and the Sub-Antarctic andmid-latitude islands. Its influence seems to be negligi-ble in our northernmost stations, located in the South

Table IV. Summary of the main mechanisms involved intemperature variability.

Decadal trend Interannual variability

East-Antarctic Global warming −SAM trend

SAM

AntarcticPeninsula

Global warming +SAM trend

?

Sub-Antarcticandmid-latitudeislands

Global warming +SAM trend

Regional SST(Agulhas current?)

Significance for ? : unknown.



Indian Ocean basin (Marion, Crozet, Kerguelen and Ams-terdam). To explain the interannual variability and thewarming there, we show that regional SST trend is amore likely candidate. These islands lie to the south ofthe Agulhas Current system, which has intensified andwarmed by up to 1.5 °C since the 1980s in responseto change in the Southern Hemisphere westerlies andincrease in trade winds in the Indian Ocean (Rouaultet al., 2009). Agulhas Current warming could have con-tributed to the warming recorded over the islands, espe-cially during north-westerly wind conditions. However,the recent strengthening of the Hadley circulation notedby Mitas and Clement (2006) and Han et al. (2010) couldalso be involved.

Due to the paucity of data in the region, it is notpossible to determine the cause of changes more con-clusively. In this regard, hindcast simulations performedin the framework of the fifth phase of the Coupled ModelIntercomparison Project could help refine and completethese results.

Acknowledgements

The authors thank for the data: Meteo-France, the SouthAfrican Weather Service, the British Antarctic SurveyReader, the New Zealand National Institute of Waterand Atmospheric Research, the Australian GovernmentBureau of Meteorology and the European Centre forMedium-Range Weather Forecasts. This is a contribu-tion to an NRF France South Africa project and to theVOASSI programme funded by CNRS. Mathieu Rouaultthanks NRF, Nansen-Tutu Centre for Marine Environ-mental Research and ACCESS for funding. Calculationswere performed using HPC resources from DSI-CCUB(Universite de Bourgogne).

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temperature changes in the mid-and high-latitudes of the southern hemisphere

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