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Instrument- and Tree-Ring-Based Estimates of the Antarctic Oscillation

JULIE M. JONES AND MARTIN WIDMANN Institute for Coastal Research, GKSS Research Centre, Geesthacht, Germany

(Manuscript received 21 March 2002, in ®nal form 1 May 2003)

ABSTRACT An estimate of the strength of the austral summer Antarctic Oscillation using station sea level pressure records for the period 1878±2000 is presented, the ®rst to the authors' knowledge. The reconstruction was obtained by relating the Antarctic Oscillation (AAO) intensity derived from NCEP±NCAR reanalysis data to the leading principal components of station records using multiple regression analysis. Particular effort has been made to ®t the model in a way that is robust to the questionable trends in the NCEP±NCAR data in the Southern Hemisphere. The trends in the reconstruction are derived from the station data, not from the NCEP data. Cross- validation with the NCEP data and comparison with other analyses of the AAO over the late instrumental period give con®dence that this station-based reconstruction can be regarded as trustworthy. With regard to the whole reconstruction period, some unquanti®able uncertainty stems from potential instability of the statistical rela- tionships. To extend this record further back, a reconstruction using tree-ring chronologies back to 1743 has also been undertaken. Comparison with the station-based reconstruction shows moderate agreement on interannual and decadal timescales, but the comparison also points toward the inherent uncertainties of proxy-based climate reconstructions. In particular, it was found that this tree-based reconstruction may have been in¯uenced by a warming that is not related to changes in the Antarctic Oscillation index during the twentieth century. Comparison of the tree-based reconstruction with a published reconstruction of zonal ¯ow over New Zealand before the twentieth century shows common features. The temperature and precipitation signals of the Antarctic Oscillation have been calculated and show that the response of the chronologies to Antarctic Oscillation variability is physically plausible. In addition, it was shown that a substantial fraction of the observed warming over much of Antarctica between the late 1950s and the 1980s can be linked to changes in the Antarctic Oscillation, whereas the observed warming over New Zealand is related to other in¯uences.

1. Introduction and regional temperatures (Briffa et al. 2001), and of the North Atlantic Oscillation (NAO) from tree rings Detection of climatic changes during the last century, (Cook et al. 2002), ice cores (Appenzeller et al. 1998), and their potential attribution to increasing concentra- and multiproxy data (Glueck and Stockton 2001; Cullen tions of atmospheric greenhouse gases and other an- et al. 2001). Such reconstructions complement the use thropogenic activities, requires a realistic estimation of of transient numerical climate models forced with past the level of natural climate variability on decadal and solar and other forcings for estimating climate vari- longer timescales. As the observational record goes back ability in the preinstrumental period. mostly only to the mid-nineteenth century, other sources This paper uses long observational sea level pressure of information on past climate variability are required. (SLP) records and tree-ring width chronologies to pro- One source of such information are palaeoclimate proxy duce reconstructions of the dominant mode of Southern data derived from natural archives, such as tree rings Hemisphere extratropical circulation, the Antarctic Os- and ice cores. These have previously been used both to cillation (AAO). The AAO, which has also been termed reconstruct the local climate, and larger-scale indices or the ``Southern Annular Mode'' (Thompson and Wallace patterns of atmospheric circulation. Reconstructions of 2000), is a zonally symmetric mode representing ex- past climate variability from proxy data include, for change of mass between the midlatitudes near 45ЊS and example, estimates of global and hemispheric (Mann et high latitudes poleward of 60ЊS, and characterizes ¯uc- al. 1998, 1999; Crowley and Lowery 2000; Briffa 2000) tuations in the strength of the circumpolar vortex. This mode has been found to be present at various atmo- spheric levels (von Storch 1999), for example, SLP Corresponding author address: Dr. Julie M. Jones, Institute for Coastal Research, GKSS Research Centre, D 21502 Geesthacht, Ger- (Gong and Wang 1999; Rogers and van Loon 1982), many. 500 hPa (Rogers and van Loon 1982; Mo and White E-mail: [email protected] 1985), and 850 hPa (Thompson and Wallace 2000).

᭧ 2003 American Meteorological Society

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The Southern Hemisphere has large areas of ocean, 1996; Kistler et al. 2001), regridded to a 5Њϫ5Њ lati- and thus few land surfaces from which to obtain proxy tude±longitude grid using a Gaussian space interpola- and observational data. This lack of data produces prob- tion technique (Santer 1988). The reanalysis involves lems in ®nding suf®cient data to produce and evaluate assimilation of surface, satellite, radiosonde, and other reconstructions. Nevertheless, a number of aspects of observations of variables including pressure, tempera- the Southern Hemisphere circulation have been recon- ture, and humidity using a global forecast model. Sev- structed using proxy data. The Trans-Polar index (TPI), eral errors have been identi®ed in this dataset in the which Pittock (1980) suggests is a measure of the dis- Southern Hemisphere. An error of 180Њ longitude was placement of the Southern Hemisphere vortex [the found in the location of the Australian surface pressure strength of which is described by the Antarctic Oscil- bogus data for the Southern Hemisphere for the period lation Index (AAOI)] towards either South America or 1979±92. These data enter the NCEP±NCAR reanalysis New Zealand, has been reconstructed by Villalba et al. and are estimates of the SLP produced by Australian (1997) using high-latitude tree-ring chronologies (the analysts using satellite data, conventional data, and time TPI has been confused with the AAOI, Folland et al. continuity for the data-poor Southern Ocean. This prob- 2001). Other dendroclimatic reconstructions include lem is considered not to be signi®cant on climatological SLP variability in the Tasman Sea area (D'Arrigo et al. timescales (Kistler et al. 2001), and is therefore not 2000), the latitude of the east Paci®c anticyclone (Vil- relevant to this work. lalba 2000), and the Southern Oscillation index (Stahle The second, potentially more serious error, are the et al. 1998). Despite interest in the AAO, as far as we possible spurious trends identi®ed by Hines et al. (2000) know, no reconstruction has been published. in the NCEP±NCAR Southern Hemisphere SLP for the We present a reconstruction of the austral summer [November±December±January (NDJ)] AAOI, using region south of 45ЊS, with the largest trend near 65ЊS. multiple linear regression between the leading principal We have therefore designed our analysis in a way that components (PCs) of standardized station SLP data and is robust against the in¯uence of the 65ЊS region and the AAOI derived from National Centers for Environ- the trends in the NCEP data. First, the southern limit mental Prediction±National Center for Atmospheric Re- of the analysis domain was chosen to be 60ЊS, to exclude search (NCEP±NCAR) reanalysis data, termed the sta- the region of strongest trends identi®ed to be at 65ЊS tion-based reconstruction (SBR). We also present a sec- by Hines et al. (2000). Second, the data were linearly ond reconstruction using the same method and tree-ring detrended at each grid point before analysis in order to chronologies from Argentina and New Zealand, the tree- remove the in¯uence of trends on the de®nition of the based reconstruction (TBR). Since the SBR is based on AAO pattern and its statistical relation to station or tree direct pressure measurements, it can be expected to have records. The northern limit of 20ЊS was chosen to be substantial skill. However it is based predominantly on consistent with the annular modes de®nitions of Thomp- data from one center of action (New Zealand and Aus- son and Wallace (2000). We consider here the NDJ tralia), which could be problematic for reconstructing AAO, but the AAO is present year-round in the tro- the intensity of a hemispheric scale pattern. Validation posphere (Thompson and Wallace 2000; Gong and against independent data for the last few decades in- Wang 1998; Thompson and Solomon 2002) and trends dicates that the reconstruction is successful. The quality in recent decades agree in all except one month (Thomp- of the SBR for the whole reconstruction depends on son et al. 2000). whether the statistical relationships derived during the calibration period hold true for the rest of the recon- struction period. Because the SBR is based on data pre- b. Station data dominantly from one center of action, the relationships Southern Hemisphere station SLP records were ob- may be less stable than if it were based on information tained from P. Jones, Climatic Research Unit, United from several centers. This adds additional, but unquan- Kingdom (Jones et al. 1999; Jones 1991). These data ti®able (outside the calibration period), uncertainty to originate mostly from the World Weather Records and it. Nonetheless we assume that the SBR is more reliable some earlier data from yearbooks from the U.K. Na- than the TBR, which is based on indirect relationships tional Meteorological Library at Bracknell, and have between tree growth and the AAOI. Thus comparison been homogenized according to Jones (1987). A total of the TBR with the SBR allows a systematic assessment of 28 stations, those with data since at least 1878, were of the uncertainties in the TBR. Given the uncertainties used. To select those containing an AAO signal, the in circulation reconstructions from proxy data found by our analysis, and also by Schmutz et al. (2000), for stations were correlated with the AAOI (see section 2d example, we regard the TBR as preliminary. for the exact de®nition). Stations were retained that were signi®cantly correlated at the 5% level, a total of 10 (Table 1). This was done for the period 1948±85 (the 2. Data and methods common period of trees and NCEP data) so that the tree a. NCEP±NCAR data and station-based reconstructions are ®tted on the same The SLP data that were used to de®ne the AAO were data. All of the rejected stations except one (Chatham) taken from the NCEP±NCAR reanalysis (Kalnay et al. were located outside of AAO centers of action.

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TABLE 1. Stations that have a statistically signi®cant relationship There are a number of possible reasons why so few with the NDJ NCEP AAOI (5% level). chronologies show a relationship with the NDJ AAOI. Station Lat, lon (Њ) First year First, chronology growth may be in¯uenced by climate 1) Tahiti Soc. Island Ϫ17.6, Ϫ149.6 1876 in other seasons. It will also be shown in section 3c that 2) Ushuaia Ϫ54.8, Ϫ68.0 1876 there are also large geographical areas that do not have 3) Perth Airport Ϫ31.9, 116.0 1876 signi®cant temperature and/or precipitation signals from 4) Sydney Airport Ϫ33.9, 151.2 1859 the AAO. For example, much of South America does 5) Hobart Airport Ϫ42.9, 147.3 1866 not have a temperature signal, thus temperature-sensi- 6) Auckland Ϫ36.9, 174.8 1853 7) Wellington Ϫ41.3, 174.8 1864 tive chronologies located in these regions would not be 8) Hokitika Aerodrome Ϫ42.7, 171.0 1866 expected to show a relationship with the AAOI. Ad- 9) Christchurch Ϫ43.5, 172.6 1864 ditionally, even in areas with strong correlations be- 10) Dunedin Ϫ45.9, 170.5 1864 tween temperature or precipitation and the AAOI on the grid-box scale (e.g., New Zealand), the relationship of local temperature and precipitation with the AAO may c. Tree-ring data differ greatly within a grid box, according to local me- teorology. Tree-ring width chronologies from Argentina, Chile, The original chronologies have been standardized to New Zealand, and Tasmania were obtained from the account for the age±growth effect, that is, as a tree ages International Tree-Ring Data Bank, together with further the growth is dispersed around an increasing radius. The Argentinean chronologies kindly supplied by R. Villal- ba. Ninety chronologies, which cover the period from standardization will have been done in different ways at least the mid-eighteenth century to 1985, were se- for many of the chronologies. To ensure consistency lected for analysis, to obtain a balance between number between chronologies, the raw ring-width chronologies of signi®cant chronologies and series length. Tree-ring for the selected trees were therefore restandardized [by data from these regions have previously been shown to C. Woodhouse, NOAA, Boulder, CO], using a cubic exhibit relationships with the large-scale atmospheric spline two-thirds of the length of the measurement series circulation (Villalba et al. 1997; D'Arrigo et al. 2000). used, which is regarded as relatively conservative (C. A similar screening process was used for the tree- Woodhouse 2001, personal communication). This stan- ring data as for the station data, using whitened (see dardization places a limit to the amount of low-fre- below) chronologies. Seven chronologies from Argen- quency variability that can be retained in tree-ring chro- tina and two from New Zealand were signi®cantly cor- nologies that is also dependent on the length of the related at the 5% level with the NDJ AAOI for the period segments making up the series (Cook et al. 1995). 1948±85 (Table 2). The pre®ltering was also undertaken Tree-ring width chronologies may contain autocor- for the December±February, December±March, and relation due to processes believed to be unrelated to February±March AAOI, however, correlations were climate, for example, stand dynamics and physiological weaker and less chronologies selected (not shown). The effects (Cook et al. 1999). Thus autoregressive modeling ®rst six chronologies are located in Tierra del Fuego has been used to remove autocorrelation from the stan- (Boninsegna et al. 1990; Roig 1998), the seventh in dardized series, to produce so-called whitened, or re- northern Patagonia (Villalba and Weblen 1997, 1998; sidual, chronologies. The level of model ®t was deter- Villalba et al. 1998). The New Zealand chronologies mined using the Akaike information criterion (Akaike were collected by Xiong and Palmer (Xiong and Palmer 1974), and ranges from AR 1 to AR 6 (Table 2). A 2000a,b) and D'Arrigo et al. (1998). The shortest of the potential problem with this approach is that climate- selected chronologies, Estancia Maria Cristina-Bosque induced low-frequency variability might also be re- Virgen, extends back to 1743 (Table 2), thus the recon- moved. It will be shown in section 3b that this is not struction extends back to this year. the case.

TABLE 2. Chronologies that have a statistically signi®cant relationship with the NDJ NCEP AAOI (5% level). Autoregressive Chronology Species Lat, lon (Њ) model level Time period 1) Estancia Carmen Camino Northofagus pumilio Ϫ54.26, Ϫ67.55 4 1730±1986 2) Estancia san Justo Northofagus pumilio Ϫ54.03, Ϫ68.34 4 1729±1985 3) Lago Yehuin Northofagus pumilio Ϫ54.28, Ϫ67.43 4 1735±1986 4) Estancia Maria Cristina-Bosque Virgen Northofagus pumilio Ϫ54.30, Ϫ67.05 4 1743±1986 5) Monte Grande, Magallanes Northofagus pumilio Ϫ53.12, Ϫ72.10 2 1681±1988 6) Peninsula Brunswick Northofagus pumilio Ϫ53.30, Ϫ71.10 6 1666±1988 7) Norquinco Austrocedrus chilensis Ϫ39.07, Ϫ71.07 2 1567±1989 8) Moa Park Libocedrus bidwillii Ϫ40.56, 172.56 1 1491±1991 9) Putara Halocarpus biformis Ϫ40.40, 175.31 1 1646±1993

Unauthenticated | Downloaded 10/03/21 12:05 AM UTC 3514 JOURNAL OF CLIMATE VOLUME 16 d. Method with respect to the period 1948±85, which is when the tree-ring and NCEP data overlap. Multiple linear regression (e.g., von Storch and To obtain the reconstruction, the station or tree-ring Zwiers 1999; Wilks 1995) was applied to estimate the width records were normalized by dividing by the stan- AAOI from the leading PCs of normalized tree-ring dard deviation from the ®tting period, and the PCs were records or station observations. This so-called principal calculated by projecting the normalized values on the component regression (PCR) was ®rst used for climate EOFs de®ned in the ®tting period. Multiplication of reconstructions by Briffa et al. (1986) to estimate the these PCs with the PCR weights derived from the ®tting leading PCs of summer SLP over England from tree- data yields the reconstruction. The reconstruction can ring PCs, and has also been used to estimate drought equivalently be expressed as a sum of weighted nor- indices from tree rings (Cook et al. 1999); and to es- malized station or tree records. These weights for the timate the winter North Atlantic Oscillation index from stations or trees are shown in the respective plots (Figs. multiproxy data (Cook et al. 2002) and from early in- 1 and 5). They were obtained by multiplying the tree strumental and documentary data (Luterbacher et al. or station EOF loadings by the PC weights. 2002). As there is a relatively short overlap period of only For the purpose of model ®tting we de®ne the AAOI 38 years (37 summers) (1948±85) between the tree-ring as the ®rst PC of detrended NCEP NDJ SLP for the records and the NCEP data, the model ®tting was per- domain 20Њ±60ЊS and the AAO pattern as the ®rst em- formed with respect to interannual variability. Calibra- pirical orthogonal function (EOF) of these data. The tion based on lower-frequency variability, although de- detrending of the NCEP data ensures that neither the sirable, would not be robust due to the small number structure of our AAO pattern nor the statistical relation of independent time steps. The degree to which our of its amplitude to the predictor variables is contami- model can estimate variability on longer timescales de- nated by potential unrealistic trends in the NCEP data. pends on the degree to which the statistical link between Note that this AAOI, which has by de®nition no trend the AAO and proxy data is not strongly timescale-de- for the ®tting period 1948±85 and therefore most likely pendent. With respect to the TBR there is also the ques- differs from the true, unknown AAOI (de®ned as the tion of how the preprocessing of the tree-ring data af- projection of perfect SLP data onto the AAO pattern), fects the reconstruction. This issue will be discussed in is only used for model ®tting. The trends in the recon- section 3b. struction are weighted means of the trends in the chro- For independent validation of the reconstruction on nologies or in the station data, which are in¯uenced by annual and on longer timescales we did not want to the true AAOI trends, and are therefore not affected by withhold more than a few years in order to keep the trends in the NCEP data. ®tting period suf®ciently long. Instead we use a cross- An overall scaling factor for the AAO pattern and validation procedure (Michaelson 1987; Wilks 1995; index can be arbitrarily chosen. Our normalization is Glueck and Stockton 2001) during which the available such that the variance of the AAO index obtained from data are repeatedly divided into calibration and short detrended NCEP seasonal means equals one for the pe- validation datasets. We performed the PCR 37 times, riod 1948±85. The physical units are associated with each time estimating a different year not included in the the AAO pattern. Local SLP signals associated with a ®tting data. These 37 individual years were then con- given AAOI can be obtained by multiplying the local catenated to produce a validation record. Using all data AAO pattern loading with the AAOI. The zero line is except the validation time step for model ®tting has been de®ned such that the AAOI obtained from detrended found to be problematic as, due to autocorrelation, the NCEP seasonal means has zero mean over the period withheld data may not be independent from the data 1948±85. used to estimate the model (Michaelson 1987). How- The predictor PCs in our analysis are derived from ever, Michaelson (1987) found lag 1 autocorrelations of NDJ seasonal means of long station records or from less than 0.25 to probably not have any measurable annually resolved tree-ring width chronologies. Again, effect on estimates of forecast skill. Because the lag 1 all records were detrended prior to analysis in order to autocorrelation of the detrended NCEP AAOI is 0.14, be consistent with the detrended predictand data. The of the detrended SBR 0.01, and of the detrended TBR station and tree-ring data were normalized to unit var- -0.05, we do not expect a substantial overestimation of iance so that records with large variance do not dominate model skill. Nevertheless, to be safe, we left out two the statistical model (i.e., the EOFs and PCs were de- years on either side of the validation year so that the rived from the correlation matrix). In order to avoid time step that is reconstructed is practically uncorrelated over®tting only the leading PCs were used for the PCR with all time steps used for model ®tting. The ®nal model. The optimal number of PCs to be retained was model used for producing the reconstructions was ®tted determined based on the correlation between the recon- using data for all 37 years. structed and the true AAOI in an independent validation The reliability of the reconstructions depends on the period. Three PCs were retained for the stations and stability of the statistical relationship between local cli- four for the trees. All EOFs and PCs were calculated mate and the AAOI throughout the entire reconstruction

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FIG. 1. The Antarctic Oscillation pattern de®ned as the ®rst EOF of detrended NDJ NCEP SLP and the station regression weights for normalized station SLP used to produce the Antarctic Oscillation index reconstruction. Isolines are in hPa, with an interval of 0.5 hPa, and show the pressure change for Antarctic Oscillation index ϩ1. The regression weights are dimensionless. The gray ®lled circles denote negative values, the black ®lled circles positive values. The circle area is proportional to magnitude. The numbers next to the circles correspond to the station numbers in Table 1.

period, this skill can not be directly quanti®ed. Prior to 3. Results the period that the relationships can be directly vali- a. The station-based reconstruction dated, instabilities can be caused by either changes in the response of the local data to the local climate or by The AAO pattern and the station weights used to changes in the relationship between the local climate obtain the reconstruction are shown in Fig. 1. The AAO and the large-scale patterns that are reconstructed (Cook pattern (detrended SLP EOF1) explains 28% of the var- et al. 2002). The ®rst source of instability is of concern iance of the detrended data and is characterized by op- only for the TBR, since the response of tree growth to posite signs at mid (approximately 45ЊS) and high lat- local climate may have changed, but not for the SBR, itudes. In the positive phase of the AAO, the westerly if we assume reasonably accurate measurements of the ¯ow at high latitudes is strengthened and that at mid seasonal SLP mean for the whole record. The second latitudes weakened, and in the negative phase this is source of instability applies to both reconstructions. Fur- reversed. ther discussion of instabilities is provided in section 3. The regression weights are positive at all stations ex- cept for Perth Airport and Ushuaia. This re¯ects the fact that all stations but these two are located within areas of positive loadings of the AAO pattern. The large com- mon weight of the Australasian stations may be expected because they are located in a center of action of the AAOI. Note that this large common weight is not a result of the large number of stations in this region. The weights are dimensionless because the input series to the PCA ®ltering prior to the multiple regression are normalized and therefore dimensionless. The coef®cient of multiple determination during the ®tting period is 0.92 (Fig. 2), thus 85% of the variance of the NDJ AAOI can be explained by the PCR model. The correlation of the reconstructed AAOI and the de- trended NCEP AAOI in the validation period is 0.91 FIG. 2. The true and reconstructed NDJ AAOI during (top) the ®tting period and (bottom) for independent validation. Black line: (statistically signi®cant at the 1% level). The reduction NCEP detrended AAOI; gray line: AAOI reconstructed from the nor- of error (RE) in the validation period is 0.82, which also malized, detrended station records. indicates good prediction skill. The RE compares the

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about 95% of the true AAOI values lay inside an interval of this size. We assume that the uncertainty remains constant back in time and that the link between predictor and predictand derived during the calibration period re- main constant throughout the reconstruction. However, there is the possibility that the relationships derived dur- ing calibration change through time. Cook et al. (2002) suggest, for example, that an anthropogenic signal may have been superimposed on the NAO or Arctic Oscil- lation (AO) signal during the twentieth century, thus making model ®tting problematic when only twentieth century data are used. Because the SBR is based pre- dominantly on information from one center of action of the AAOI, it is possibly more at risk from instability in the relationship between the predictors and the AAOI than if it were based on data from several centers, thus

FIG. 3. The NDJ AAOI from NCEP SLP (trend included) (solid is subject to more uncertainty than suggested by the black line), the tree-based reconstruction (TBR; dashed line) and the validation statistics and the con®dence intervals. Inclu- station-based reconstruction (SBR; gray line). The years are dated by sion of stations from additional centers of action may Nov/Dec. reduce the uncertainty. Station measurements from the other main AAO centre of action (the Antarctic) start ®rst in the early twentieth century. residuals from our reconstruction (validation series) It can be seen from Fig. 4 that the SBR shows a period with the residuals relative to an estimate based on no of dominantly negative AAOI from around 1900 up to knowledge, which here is taken as the calibration period the late 1950s, then a period of more positive values mean of the predictand (Fritts et al. 1990; Cook et al. during the 1960s, followed by a trend to negative values 1999). The RE can have values from Ϫϱ to ϩ1, with until 1980, and then again to positive values until the positive values indicating skill relative to the no-knowl- end of the record. edge estimate, and a value of ϩ1 indicating perfect reconstruction for the validation period. Some overop- b. The tree-based reconstruction timistic comparison may be expected because most of the predictor stations have also entered the NCEP re- The AAO pattern (the same as in Fig. 1) and the tree- analysis, but this effect is probably small because nu- ring weights used to obtain the TBR are shown in Fig. merous other stations, including those from other cen- 5. The total in¯uence of the New Zealand chronologies ters of action such as the Antarctic, also enter the re- is similar to that of the South American chronologies, analysis. Although a very good reconstruction skill thus in contrast to the SBR, the TBR is based on in- could be expected as direct pressure measurements have formation from two centers of action of the AAO. The been used, it is noteworthy that the AAOI can be re- coef®cient of multiple determination during the ®tting constructed so well during the calibration period from period is 0.72 (signi®cant at the 1% level) (Fig. 6), thus stations dominantly located in New Zealand and Aus- 52% of the variance of the AAOI is explained by the tralia. regression model. The correlation between the recon- Station data for the period after 1985 are completely structed AAOI and the detrended NCEP AAOI in the independent, as they were not used for ®tting the model, validation period is 0.66. The RE of 0.46 indicates mod- thus the SBR for this period can also be compared on erate skill relative to a no-knowledge prediction, but an interannual basis with the NCEP AAOI for model shows that the TBR has lower skill than the SBR. evaluation. Figure 3 shows the SBR over this period To determine whether the tree growth/climate re- (and also the TBR) compared to the NCEP AAOI, which sponse implied by the regression relationships are con- was calculated by projecting the NCEP SLP data onto sistent with the local climate signal of the AAO, and the 1948±85 EOF. This PC agrees well with the SBR the local tree response for the chronologies where this over the period 1986±98, with a correlation of 0.69 is known, we calculated correlation maps between the (statistically signi®cant at the 1% level) and an RE of AAOI and detrended temperature or precipitation, using 0.54. NCEP 850-hPa temperatures and the precipitation da- The SBR is shown in Fig. 4 (top). The black line is taset of New et al. (1999, 2000), respectively. The tem- a 9-yr running mean. The error bars are con®dence in- perature map shows coherent large-scale features (Fig. tervals, which cover the true value with a probability 7) with positive correlations in the regions of positive of 95%. They are de®ned as Ϯ1.96 standard deviations AAO loadings in the mid/southern ocean basins, in- of the residuals from the model ®tting because during cluding over New Zealand, and a weakly positive signal the ®tting period and for normally distributed residuals over South America. The surface precipitation signal of

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FIG. 4. The reconstructed NDJ AAOI. Bars show the (top) station-based reconstruction (SBR), and (bottom) the tree-based reconstruction (TBR). The solid black line is the 9-yr running mean. The thin lines show the 95% con®dence intervals. The years are dated by the Nov/Dec. the AAOI (Fig. 8) also shows large areas with a rela- being part of the 5% which may be selected by chance tively coherent signal, including South Island, New Zea- in the pre®ltering. land, although less so than temperature, as precipitation Norquinco, the northernmost of the South American is more spatially variable and also some grid boxes may chronologies, is located in a region of negative precip- contain few input data. itation and weakly positive temperature correlation co- Of the two New Zealand chronologies, Moa Park has ef®cients (Figs. 8 and 7), indicating increased precipi- a negative weight (Fig. 5), suggesting decreased (in- tation and lower temperatures due to low pressure to creased) growth with a positive (negative) index, where- the west of South America during the negative phase as the positive weight of Putara indicates the opposite of the AAOI, during which the negative weight of this response. The response of these chronologies to local chronology suggests increased growth and vice versa. climate is known. Putara, together with other pink pine This is in agreement with Villalba and Weblen (1997), chronologies, is positively correlated with November± who also found growth for this chronology to be strong- April local station temperatures and New Zealand av- ly positively correlated with spring and early summer erage temperatures (D'Arrigo et al. 1998). Moa Park, precipitation and negatively correlated with temperature conversely, is negatively correlated with New Zealand during the current growing season. average December temperatures (Xiong and Palmer The northernmost chronologies of Tierra del Fuego 2000a). The AAOI temperature and precipitation cor- (Peninsula Brunswick and Monte Grande Magallanes) relation maps (Figs. 7 and 8) show that lower pressure appear to be precipitation sensitive, as they are located over New Zealand in the negative AAOI phase is as- in an area of signi®cant negative precipitation correla- sociated with reduced temperatures and increased pre- tions and of insigni®cant temperature correlations. The cipitation over South Island, which is characteristic of negative regression weights thus indicate increased midlatitude maritime climates. The relationship of the growth with increased precipitation. The local precipi- chronologies with climate implied by the regression tation response to the AAO in this region (Fig. 8) we weights is thus physically sensible, indicating that these surmise is due to the fact that that a negative AAO is two chronologies are truly related to the AAOI, rather associated with decreased westerly/increased easterly

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FIG. 5. The NDJ AAO pattern (as in Fig. 1) and the tree regression weights for normalized tree-ring chronologies. Isolines and circles as in Fig. 1. The numbers next to the circles correspond to the chronology numbers in Table 2.

¯ow, which results in increased precipitation, possibly due to the orographic effect of mountains in the south- west of Tierra del Fuego. The other Tierra del Fuego chronologies, Estancia Carmen Camino, Estancia San Justo, Lago Yehuin, and Estancia Maria Cristina-Bosque Virgen, are located in the drier part of the island to the north of mountains (Boninsegna et al. 1990). A positive relationship with late spring and summer precipitation was found for a regional chronology that includes these chronologies as well as some positive relationships with summer temperatures (Boninsegna et al. 1990). Because the AAOI temperature and precipitation correlation co- ef®cients (Figs. 7 and 8) are very small, it appears that they do not fully capture the effect of the AAOI on the

FIG. 7. Correlation map between NCAR±NCEP 850-hPa temper- FIG. 6. As in Fig. 2 except gray line: NDJ AAOI derived from the ature and the detrended NCEP AAOI (NDJ 1948±98). The chronology normalized, detrended ring-width chronologies. locations are marked with a cross.

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FIG. 8. Correlation map between the precipitation of New et al. (2000) and the detrended NCEP AAOI (NDJ 1948±98). The chronology locations are marked with a cross. local climate, which in turn affects tree growth. How- certain. Additionally, as stated in section 2c, variation ever, this region suffers from a sparsity of instrumental can be expected within a grid box, particularly in regions records (J. A. Boninsegna 2001, personal communica- such as this with complex topography. tion), thus the gridded climatologies are relatively un- The TBR [Fig. 9 and Fig. 4 (lower)] was produced

FIG. 9. The tree-based NDJ AAOI reconstruction. The solid black line is a 9-yr running mean. The thin lines show the 95% con®dence intervals. The years are dated by the Nov/Dec.

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data from one center of action, we assume that the SBR is closer to the true AAOI than the TBR. Based on this assumption, we can validate the TBR against the SBR over the full period 1878±1985, therefore allowing also a validation of the low-frequency variability. Given the various error sources for proxy-based re- constructions, the correlations between the SBR and the TBR are encouraging, 0.43 for the full period 1878± 1985, 0.50 since 1900, and 0.56 for the ®tting period (1948±85) (all signi®cant at the 1% level). Although the signi®cance of the correlations shows that the SBR and the TBR are clearly related, their relatively low values re¯ect the substantial differences between the two re- constructions. Despite these differences, the SBR and TBR can be regarded as consistent, because their 95% con®dence intervals overlap for all but four time steps. The low-frequency variability, as represented by the FIG. 10. The autocorrelations of the detrended NDJ NCEP AAOI (solid black line), the tree-based reconstruction (dashed black line), 9-yr running mean (Fig. 4), shows some common fea- the station-based reconstruction (solid gray line), and the unprewhi- tures, although the correlation over the whole period is tened tree-based reconstruction (dashed gray line). only 0.26 (signi®cant at the 1% level) and 0.38 for the ®tting period (signi®cant at the 5% level). The minima at around 1900 and 1940±50 are present in both recon- by multiplying the prewhitened tree-ring width series structions, as is the peak between 1930 and 1940, and by the regression weights. The uncertainty bands on this the trend from positive to negative index between 1960 reconstruction are much larger than those of the SBR and 1980. The main difference is that the TBR shows because of the lower fraction of AAOI variance ex- a positive trend from 1900 to the late 1950s. This up- plained by the model (52% for the TBR compared to ward trend is less evident in the SBR, where the period 86% for the SBR). Figure 9 shows that the decadal- from 1900 until the mid-1950s is dominated by negative scale signal in the TBR is less variable prior to 1860 values, followed by a period of positive index for ®ve than after. The decade from 1870 to 1880 has the lowest years, before a return to more negative values (Fig. 4). running mean index, there is then an upward trend to a The linear trends over the period 1900±60 are 0.008 maximum in the mid-1950s, followed by a trend towards yrϪ1 for the TBR and 0.003 yrϪ1 for the SBR. This a negative index until the record ends in the mid-1980s. discrepancy shall be discussed further in section 4. This reconstruction shall be further discussed later. Removing autoregression from a time series by pre- whitening can reduce the low-frequency variability con- 4. Discussion tained in that series, and hence any reconstruction de- A number of investigations of Southern Hemisphere rived from this series. To investigate whether the pre- circulation for recent decades have been undertaken, but whitening has in¯uenced the low-frequency variability no other studies have investigated the behavior of the in the reconstruction, a reconstruction was also pro- AAO for the pre-NCEP period. We use these studies, duced using the unprewhitenend chronologies (not particularly those that use other atmospheric data than shown). The interannual correlation between these two the NCEP±NCAR SLP, as a consistency check for the reconstructions is 0.76, and between the 9-yr running SBR. means 0.84, and the features in the TBR described above The trends in the SBR are compatible with the anal- are present. Thus the prewhitening has not changed the yses of Thompson et al. (2000) and Chen and Yen main features of the TBR. The NCEP AAOI contains (1997) over their common periods. The former identi- little autocorrelation (Fig. 10) and the TBR autocorre- ®ed a trend towards high index polarity of their Southern lation is much closer to the NCEP autocorrelation at Hemisphere annular mode, based on 850-hPa NCEP most lags than the unprewhitened reconstruction, thus geopotential height (GPH), over the period 1968±97, in we consider the TBR to be more reliable than the un- all months except June. Similarly, using Australian Bu- prewhitened reconstruction. reau of Meteorology (ABM) data for the period 1972± 92, the latter identi®ed a deepening of the circumpolar c. Comparison of the tree-ring- and station-based trough and an increase in midlatitude SLP in summer. reconstructions Both correspond to a positive AAOI. The linear trends in the SBR over the periods 1968±97 and 1972±92 are The validation of the SBR in section 3a shows that indeed positive (0.01 and 0.02 yrϪ1) respectively. It is the SBR has considerable skill. Despite the uncertainties important to reiterate that, because the SBR and TBR in the SBR related to the fact that it is based mainly on were calibrated against the detrended NCEP data on an

Unauthenticated | Downloaded 10/03/21 12:05 AM UTC 1NOVEMBER 2003 JONES AND WIDMANN 3521 annual basis and because the reconstructions are pro- give a very small temperature increase of 0.04Њ±0.07ЊC duced using undetrended station data and chronologies, over the period 1900±60. the trends in the two reconstructions are those contained We therefore suggest that the temperature-sensitive in the trees and the stations and not those in the NCEP New Zealand chronologies may be responding to a local data. temperature increase that is unrelated to the AAOI, lead- Analyses of shorter intervals within the reconstruc- ing to a positive trend in the TBR over the period 1900± tion period are also encouraging. A decrease in the 60. However, this hypothesis is tentative, ®rst, because strength of westerly ¯ow at Southern Hemisphere high the trend in the TBR is small and not signi®cant and, latitudes measured as DJF 500-hPa height differences second, because other trends may also be possible if one between Chatham Island and McMurdo, Antarctica, was takes into account the con®dence intervals. noted between 1957/58 and 1978/79 (Rogers and van Given the uncertainties in the TBR during the twen- Loon 1982), which is evident in both the SBR and the tieth century, an important question is how the TBR TBR. There is also agreement between the PC1 of Mo compares to other climate reconstructions over New and White (1985) from Australian analysis Southern Zealand in the period before the documented non-AAO- Hemisphere 500-hPa height anomalies for the period related warming over New Zealand. As there are no 1973±80 and the SBR and to some extent with the TBR reconstructions of the AAOI, we have to turn to recon- (e.g., low AAOI summer 1976/77, high AAOI summer structions of other circulation features that may be re- 1973/74). Thus our claim that the SBR is trustworthy, lated to the AAO. Salinger et al. (1994) produced re- based on comparison with the detrended NCEP AAOI, constructions of zonal ¯ow (the Auckland±Christchurch is corroborated by these additional comparisons, and pressure gradient) from New Zealand tree-ring chro- conversely the SBR con®rms the ®ndings of these stud- nologies. During the pre-1900 period a number of fea- ies. tures in the TBR are also present in this zonal ¯ow The undetrended NCEP AAOI, as discussed previ- reconstruction, namely the minima between 1760 and ously, agrees relatively well with the TBR and SBR on 1770 and between 1820 and 1830. The minima in the an interannual basis, both for the ®tting period and for TBR during the 1860s and 1870s are also present, al- the independent period of the TBR (1985±98) (Fig. 3). though to a lesser extent, in the Salinger et al. (1994) However, ®tting a linear trend to the NCEP AAOI gives reconstruction. This may suggest that some con®dence can be placed in the TBR before the twentieth century. larger trends than for the SBR, of 0.03 yrϪ1 over the However, the Salinger reconstruction is based on chro- period 1968±97 and of 0.06 yrϪ1 over the period 1972± nologies that are sensitive to temperature (as well as to 92. We cannot determine whether this is due to spurious precipitation), so it is also subject to potential changes trends in the NCEP data or because the SBR does not in the link between the local climate and large-scale fully capture the true trend. circulation. It should be noted that none of the chro- The larger positive trend in the TBR than in the SBR nologies are common between those used to construct over the period 1900±60 identi®ed in section 3c can the TBR and those used by Salinger et al. (1994). possibly be explained by the observed increase in New Another Southern Hemisphere circulation index, that Zealand temperatures since the beginning of the twen- has been reconstructed is the TPI (Villalba et al. 1997). tieth century, the rate of which reduced during the 1970s It represents the latitudinal displacement of the circum- (Folland and Salinger 1995). The New Zealand chro- polar vortex, and was de®ned by Pittock (1980) as an nologies, Moa Park and Putara, have negative and pos- index of pressure differences between Hobart and Stan- itive responses to temperature, respectively (section 3b). ley and by Villalba et al. (1997) as an index of differ- The tree regression weights (Fig. 5) are negative and ences between pressure over the New Zealand and the positive for the former and the latter, respectively. Thus South America±Antarctic Peninsula sectors of the a temperature increase over New Zealand would result Southern Ocean. The relationship between the AAOI in an increased reconstructed AAOI, assuming no com- and the TPI seems to be unclear. The fact that the ®rst pensating trends caused by the Argentinean chronolo- of these two locations is located in a positive center of gies. action of the AAO, and the second in the negative center, Because of the lower positive trend in the SBR over indicates that correlation between the TPI and the AAOI this period, we can conclude, however, that the observed is likely. However, Rogers and van Loon (1982) suggest temperature changes cannot be related to changes in the that the TPI is associated with their summer SLP PC2. strength of the AAO, thus either must have resulted from Thus the TPI would be expected to have low correlations another mode of circulation, for example ENSO (Fol- with their PC1 (and thus with the analogous AAOI). land and Salinger 1995), and/or from noncirculation- The comparison of the TPI and the TBR indeed shows related causes, for which anthropogenic warming is a similarities, for example, the upward trend between candidate. This is also backed up by the regression co- 1900 and the mid-1950s, and the subsequent downward ef®cients between the AAOI and temperature over New trend between the mid 1950s and 1980s, and between Zealand of 0.2±0.4 (not shown), from which it can be 1780 and 1830. For other periods however, for example, calculated that the trend in the SBR of 0.003 yrϪ1 would between 1860 and 1890, the agreement is not so good.

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Three of the nine chronologies used here were among Ϫ0.4 to Ϫ0.5 in this region. Combined with the AAOI the 12 used by Villalba et al. (1997) to produce their in this year of 2.35, this would give a warming above reconstruction. This may lead to some commonality be- the ®tting period mean of 0.94Њ±1.18ЊC. tween the reconstructions in the case that the relative Regarding the New Zealand regional atmospheric cir- weights of the three common chronologies in both re- culation, it appears that the AAO has had a strong in- constructions are similar. ¯uence on the circulation over this region in the period The AAO is also connected to temperatures over 1930±75 and had a weak in¯uence in the period 1976± much of Antarctica. The links between the AAOI and 94. Salinger and Mullan (1999) used station records temperature evident from the correlation map in Fig. 7 from the National Institute of Water and Atmospheric enable investigation of whether climate variability in Research to investigate circulation changes over New this region could be related to changes in the AAOI. Zealand. They found that the period 1930±94 could be Much of the Antarctic has negative temperature corre- divided into three circulation periods: 1930±50 with lation coef®cients with the AAOI. Thus increased (de- anomalous south to southwest air¯ow, 1951±75 with creased) temperatures over these areas are related to a increased easterly and northeasterly air¯ow, and 1976± decreased (increased) AAOI. Note that this excludes the 94 with west to southwesterly air¯ow predominating. Antarctic Peninsula, which has been found to exhibit The ®rst period is indeed one of dominantly negative differing behavior to much of the rest of the continent AAOI in the SBR (Fig. 4), which brings westerly or (Rogers 1983). These correlations agree with the results southwesterly ¯ow to New Zealand (Fig. 1). The second of Rogers and van Loon (1982), who found that higher interval shows a shift to a positive AAOI, that is, east- (lower) temperatures at Amundsen±Scott Station oc- erly anomalies in both reconstructed series, again agree- curred in years with weak (strong) Southern Hemisphere ing with Salinger and Mullan. The ®nal period however westerlies during both summer and winter. This link shows a short period of westerly anomalies and then a between the strength of the southern westerlies and Ant- move to easterly anomalies, suggesting that another cli- arctic temperatures was also found by Raper et al. matic in¯uence must be overriding the AAO. (1984). Instrumental temperature measurements over the Ant- 5. Conclusions arctic between 1957 and 1982 showed a statistically signi®cant positive trend in DJF of 0.032ЊCyrϪ1 (Raper A reconstruction of the austral summer (Nov±Jan) et al. 1984), consistent with the negative AAOI trend Antarctic Oscillation index (AAOI) has been produced in the SBR, indicating that at least some of this warming for the period 1878±2000 from long station SLP records could be related to AAO-related circulation changes. (termed the SBR). This reconstruction can be regarded We can estimate the warming that could be expected as relatively reliable because of the high coef®cient of from the in¯uence of the AAO over this period from multiple determination in the ®tting period and the high regression coef®cients calculated between the NCEP coef®cient of multiple determination and the high re- AAOI and NCEP 850-hPa temperature. The trend cal- duction of error during the validation period. The SBR culated over 1957±82 in the SBR is Ϫ0.037 yrϪ1. The is also in agreement with trends in the AAOI from other regression coef®cient over much of Antarctica varies datasets for the period for which comparison is possible. between Ϫ0.2 and Ϫ0.6 (not shown), thus, depending This is the ®rst time that the behavior of the AAO has on the location, the warming associated with this trend been discussed for a period longer than that of the NCEP in the AAOI would be between 0.007ЊCyrϪ1 and or the Australian Bureau of Meteorology data of ap- 0.022ЊCyrϪ1, that is, 22% to 69% of the observed proximately 50 years. The SBR shows that for the ®rst warming can be linked to AAOI changes when we as- half of the twentieth century the NDJ AAOI was dom- sume that the observed trend at all locations is the same inantly negative, changing to a period of positive values as the trend found by Raper et al. (1984). during the 1960s, followed by a trend to negative values The in¯uence of the AAO on Antarctic climate in until 1980, then by a trend towards positive values from individual years is also apparent from the reconstruc- 1980 until the end of the reconstruction. However, when tions. An interesting result is the presence of the lowest interpreting the SBR outside of the calibration period, value of the SBR series in summer 1911/12, which is the increased uncertainty related to the stability of the also one of the lowest TBR values. From the correlation underlying regression model outside of the calibration map relationships described above one would expect period, which is particularly pertinent here because the increased temperatures over much of Antarctica. Indeed, SBR is based dominantly on stations from one center this year was described as having dif®cult weather con- of action of the AAO, should be borne in mind. ditions, with unusually high temperatures, for the Scott A second reconstruction has been developed using Expedition to the South Pole (Schwerdtfeger 1984; Vil- tree-ring width chronologies from New Zealand and lalba et al. 1997), with temperatures of ϩ1.1ЊC over the South America for the period 1743±1985 (termed the Beardmore Glacier (83Њ20ЈS, 170ЊW). We can again es- TBR). The tree±climate relationships implied by the re- timate the temperature anomaly expected from the SBR gression model are physically sensible, agreeing in AAOI value using the regression coef®cients, which are many cases with relationships found in published lit-

Unauthenticated | Downloaded 10/03/21 12:05 AM UTC 1NOVEMBER 2003 JONES AND WIDMANN 3523 erature on the individual chronologies. As high con®- also thank NCEP±NCAR for providing the reanalysis dence can be placed in the SBR in comparison to the data, and the Climatic Research Unit for the precipi- TBR, the former has been used to evaluate the latter. tation dataset. We also thank Hans von Storch for useful Signi®cant correlations on interannual and longer time- discussion. Thanks also to Nikolaus Groll and to Beate scales were found, but also clear differences. For ex- Gardeike for help with ®gure preparation. This work ample, the TBR contains an upward trend from the be- was funded by the Helmholz Gemeinschaft under KIHZ ginning of the twentieth century until 1960 that is weak- (Climate in Historical Times) and by the Federal Min- er in the SBR. Differences between the two reconstruc- istry of Education and Research under the DEKLIM tions may be partly due to the different spatial programme (Deutsches Klimaforschungsprogamm). distribution of the stations and the chronologies, but may also be caused by nonclimatic in¯uences on the REFERENCES chronologies or by temperature or precipitation changes that are unrelated to the AAO. We hypothesise that this Akaike, H., 1974: A new look at the statistical model identi®cation. TBR trend, which corresponds to a period of observed IEEE Trans. Autom. Control, AC-19, 716±723. temperature increase over New Zealand, may be a result Appenzeller, C., T. F. Stocker, and M. Anklin, 1998: North Atlantic Oscillation dynamics recorded in Greenland ice cores. Science, of increased growth of the temperature-sensitive New 282, 446±449. Zealand chronologies caused by a non-AAO-induced Boninsegna, J. A., J. Keegan, G. C. Jacoby, R. D'Arrigo, and R. 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