Non-uniform interhemispheric temperature trends over the past550 years

Richard P. Duncan • Pavla Fenwick •

Jonathan G. Palmer • Matt S. McGlone •

Chris S. M. Turney

Received: 26 March 2009 / Accepted: 10 March 2010 / Published online: 28 March 2010

� Springer-Verlag 2010

Abstract The warming trend over the last century in the

northern hemisphere (NH) was interrupted by cooling from

AD 1940 to 1975, a period during which the southern

hemisphere experienced pronounced warming. The cause

of these departures from steady warming at multidecadal

timescales are unclear; the prevailing explanation is that

they are driven by non-uniformity in external forcings but

recent models suggest internal climate drivers may play a

key role. Paleoclimate datasets can help provide a long-

term perspective. Here we use tree-rings to reconstruct

New Zealand mean annual temperature over the last

550 years and demonstrate that this has frequently cycled

out-of-phase with NH mean annual temperature at a perio-

dicity of around 30–60 years. Hence, observed multideca-

dal fluctuations around the recent warming trend have

precedents in the past, strongly implicating natural climate

variation as their cause. We consider the implications of

these changes in understanding and modelling future cli-

mate change.

Keywords New Zealand � Dendrochronology �Atlantic multidecadal oscillation � Interdecadal Pacific

Oscillation � Interhemispheric temperature variability �Multi-decadal temperature change

1 Introduction

Climate forecasts predict a rise in global temperatures over

the coming decades driven by increasing anthropogenic

input of greenhouse gases (IPCC 2007). Nevertheless,

while greenhouse gas concentrations rose steadily during

the twentieth century, temperatures did not. In the northern

hemisphere (NH) warming was interrupted by cooling from

AD 1940–1975, a period during which the southern hemi-

sphere (SH) began to warm more rapidly (Brohan et al.

2006). The prevailing explanation for these deviations from

steady warming is non-uniformity in radiative forcing due

to solar variations, volcanoes and aerosols (Tett et al. 1999;

Stott et al. 2000). Cooling in the NH from AD 1940–1975

has been attributed to sulphate aerosols from human

activity, which may have cooled the NH sufficiently to

interrupt anthropogenic warming (Stott 2003). Hemi-

spheric differences in the pattern of warming have likewise

been attributed to spatial variation in the impact of external

forcing agents, including those affected by human activities

(Kaufmann and Stern 2002; Stott 2003).

Nevertheless, there is evidence that internal natural cli-

mate oscillations may contribute to or even dominate

hemispheric temperature variations at multidecadal

(Delworth and Mann 2000; Andronova and Schlesinger

2000; Parker et al. 2007; Smith et al. 2007) and longer time-

scales (Schaefer et al. 2009). The two dominant modes of

natural variation at multi-decadal scales are the Interdecadal

Pacific (IPO) and Atlantic Multidecadal Oscillations

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00382-010-0794-2) contains supplementarymaterial, which is available to authorized users.

R. P. Duncan (&) � M. S. McGlone

Landcare Research, PO Box 40, Lincoln 7640, New Zealand

e-mail: Richard.Duncan@lincoln.ac.nz

R. P. Duncan

Bio-Protection Research Centre, Lincoln University,

PO Box 84, Lincoln 7647, New Zealand

P. Fenwick � J. G. Palmer

Gondwana Tree-ring Laboratory, PO Box 14, Little River,

Canterbury 7546, New Zealand

C. S. M. Turney

School of Geography, University of Exeter, Exeter EX4 4RJ, UK

123

Clim Dyn (2010) 35:1429–1438

DOI 10.1007/s00382-010-0794-2

(AMO) (Parker et al. 2007). While there are uncertainties

over the mechanisms underlying these internal oscillations,

they appear linked to ocean circulation patterns (Knight

et al. 2005), and recent effort to incorporate this variation

into climate models has resulted in more accurate hindcasts

of temperature over the latter half of the twentieth century

(Smith et al. 2007; Keenlyside et al. 2008).

Variation in the IPO has been linked to the AMO (Zhang

and Delworth 2007) with variability in the latter strongly

manifested as contrasting temperature trends between the

northern and southern hemispheres (Mann and Park 1994;

Parker et al. 2007). If these internal oscillations drive

hemispheric temperature fluctuations at multidecadal and

longer timescales then this should be apparent as charac-

teristic deviations in temperature records, specifically with

a strong north–south hemispheric contrast. Evidence for

this during the mid- to late Holocene comes from com-

parisons between New Zealand (NZ) and the NH in glacier

length fluctuations (Schaefer et al. 2009). These show

periods when NZ and NH glaciers fluctuated in-phase and

periods when they were out-of-phase, inconsistent with

scenarios in which a global forcing agent causes hemi-

spheric changes to be consistently in-phase, or in which

consistent antiphase changes are driven by hemispheric

offsets in the strength of deep-water formation (Broecker

1998). Instead, Schaefer et al. (2009) suggest that a pattern

of in-phase, out-of-phase fluctuations are more likely

caused by regional climate drivers, specifically the AMO

and IPO. They observe that during the twentieth century

glacier length fluctuations in the Swiss Alps closely mat-

ched AMO variations (Denton and Broecker 2008), while

at the same time glacier length fluctuations in New Zealand

were linked to IPO variations (Schaefer et al. 2009).

In this paper we develop a new tree-ring chronology for

New Zealand that allows us to reconstruct NZ mean annual

temperature back to AD 1450, and use this reconstruction

to explore temperature variations in New Zealand and the

NH over the last 550 years. We show that NZ and NH

temperatures have intermittently fluctuated out-of-phase at

multidecadal timescales, matching the pattern seen in

Schaefer et al. (2009) over longer timescales, and that these

fluctuations are linked to variations in the AMO and IPO.

2 Methods

2.1 Site selection and chronology construction

We developed a new tree-ring chronology for Halocarpus

biformis, a long-lived conifer species native to New Zea-

land whose tree-rings are known to be temperature sensi-

tive (Dunwiddie 1979; D’Arrigo et al. 1998; Xiong et al.

1998). We used existing ring width data for H. biformis

from three studies (Dunwiddie 1979; D’Arrigo et al. 1998;

Xiong et al. 1998), for which the raw data were down-

loaded from the International Tree-Ring Data Bank (http://

www.ncdc.noaa.gov/paleo/treering.html), and collected

ring width data for H. biformis from an additional 13 sites

located throughout South Island and lower North Island,

New Zealand that covered the distributional range of the

species (Fig. 1; Table 1). All but two sites were at high

altitude close to natural tree lines, increasing the likelihood

of obtaining temperature sensitive tree-ring series (Stokes

and Smiley 1968). The two sites at lower elevation were at

higher latitude and showed a clear temperature signal.

We used increment borers to collect core samples

(typically two per tree) from an average of 28 trees (range

16–53) at each of the 13 new sites, with all trees having a

diameter at 1.4 m C 20 cm. Cores were mounted, sanded

and dated following standard dendrochronological proce-

dures (Stokes and Smiley 1968). Because the New Zealand

growing season covers two calendar years, we assigned

rings to the year in which growth began so that the final

ring in each series was from the 1999/2000 summer, which

was dated as 1999. We used a combination of visual

(Stokes and Smiley 1968) and computer-aided (Holmes

1983) techniques to crossdate the ring-width series and

were able to crossdate 90% of them. The frequency of

missing rings in the series was low (0.065%). We obtained

ring width measurements from a total of 674 cross-dated

tree-ring series. The number of sites and the number of

series used in chronology construction in each year are

shown in Fig. 1b, c.

We constructed a chronology by fitting an age related

growth curve to each series and extracting an index of the

common non-age related growth signal in each year using a

hierarchical regression model (Gelman and Hill 2007).

We removed the age-related growth trend in the ring-

width series by fitting growth curves using the following

equation (Zeide 1993):

rwijk ¼ b0ijdiamb1ij

ijk eb2ijageijk

where: rwijk is the ring width of series i at site j in calendar

year k.; ageijk is the cambial age of tree i at site j in

calendar year k; diamijk is the diameter of tree i at site j in

calendar year k (calculated assuming that the diameter is 0

at cambial age = 0); b0ij, b1ij and b2ij are parameters

estimated for tree i at site j that define the shape of the

curve.

We used this equation because it generalizes a series of

curves that are widely used to model sigmoidal tree growth

(Zeide 1993), including the Gompertz (b1ij = b2ij = 1),

logistic (b1ij = 2, b2ij = 1), Bertalanffy (b1ij = 2/3,

b2ij = 1) and Chapman–Richards (b2ij = 1) equations. In

1430 R. P. Duncan et al.: Non-uniform interhemispheric temperature trends

123

addition, this equation can model negative exponential

(b1ij \ 0, b2ij = 0) and constant growth (b1ij = b2ij = 0).

This equation provided a substantially better fit to our data

(based on Akaike’s Information Criterion) than the

Hugershoff equation, which is commonly used to model

age-related growth trends in dendrochronology (Briffa

et al. 1998).

The conventional method of chronology construction is

to fit age-related growth curves to tree-ring series, extract

the deviations from those curves (either as residuals or as a

ratio), and then average those deviations by calendar year

to estimate the common (non-age-related) growth signal in

each year (Cook et al. 1990). A feature of data used in

chronology construction is that the number of measure-

ments available from which to estimate the common signal

in each year varies. When cores are taken from living trees

there are typically fewer measurements available further

back in time because progressively fewer old trees are

available to sample. A consequence of this sampling vari-

ation is that the average of the deviations in a calendar year

having few measurements contains less information about

the common growth signal in that year, and is likely to be

165 170 175

-46

-44

-42

-40

-38

-36

-34

Longitude (E)La

titud

e (S

)

(a) (b)

(c)

(d)

1400 1500 1600 1700 1800 1900 2000

1400 1500 1600 1700 1800 1900 2000

05

15

Num

ber

of s

ites

030

060

0

Year

Num

ber

of s

erie

s

1400 1500 1600 1700 1800 1900 2000

0.6

0.7

0.8

0.9

1.0

Year

EP

S

Fig. 1 a Location of the 16

sites used in constructing the

Halocarpus biformischronology. The absence of

sites in the central and upper

North Island, the eastern South

Island and between

approximately 43 and 45�S are

due to range limits or represent

gaps in the distribution of the

species. b The number of sites

from which series were

available in each year. c The

number of series available in

each year. d The expressed

population signal (EPS), a

measure of chronology

reliability calculated using a

50-year window with 25-year

overlap. The dashed line is at

0.85

Table 1 Characteristics of the

13 new Halocarpus biformissites used in chronology

construction

Site Latitude (S) Longitude

(E)

Altitude

(m a.s.l.)

No. of crossdated

trees

No. of crossdated

series

Mt. Bonar 43�050 170�390 850 25 51

Camp Creek 42�430 171�340 970 53 72

Croesus Track 42�170 171�230 900 35 52

Eldrig Peak 45�450 167�280 750 26 52

Mt. Glasgow 41�370 172�020 950 26 48

Matiri Range 41�340 172�190 1,060 28 51

Mt. Elliot 42�300 171�500 1,050 16 30

Mt. Greenland 42�570 170�490 865 26 48

Mt. French 42�400 171�200 750 31 56

Omoeroa Saddle 43�240 170�060 320 20 34

Slopedown Hill 46�220 169�030 560 12 25

Mt. Te Kinga 42�390 171�300 950 27 47

Totara Saddle 42�590 170�510 210 20 32

R. P. Duncan et al.: Non-uniform interhemispheric temperature trends 1431

123

less reliable and to exhibit greater variation, than the

average of the deviations in a year having more measure-

ments. This means that variation in the common growth

signal among years will tend to be overstated and some

years will appear more extreme than they actually are.

This problem is well recognised and procedures for

processing a chronology to adjust the variance accounting

for differing sample sizes have been proposed (Osborn

et al. 1997). An alternative approach, which addresses this

problem directly, is to use hierarchical regression model-

ling. Following the conventional approach, the common

growth signal in the kth year, ak, is estimated as the average

of the deviations from the age-related growth curve in that

year, and is free to take any value. Using a hierarchical

approach, deviations from the age-related growth curve are

assumed to derive from some larger population of values

modelled as being drawn from a probability distribution.

This has the effect of weighting the ak estimates by sample

size and hence the amount of information available in each

year (Gelman and Hill 2007). When years contain very few

measurements their estimates will tend to be pulled closer

to the overall mean reflecting the fact that we have less

information about the common growth signal in that year.

This prevents the problem of estimating more extreme

values in certain years due to small sample sizes.

We used a linear form of the growth equation above,

obtained by taking logs. We then included three terms to

partition deviations away from age-related growth curves

into three components. The first, cjk, modelled the com-

ponent of deviations in the kth year common to all series

at the jth site, and was included because series from the

same site may share a common growth signal reflecting

the particular site conditions. The cjk were modelled as

normally distributed with mean 0 and variance rc2. The

second term, ak, modelled the component of deviations in

the kth year common to all series, having accounted for

the common growth signal among series at any particular

site, with the ak modelled as normally distributed with

mean 0 and variance ra2. Hence in year k, if all series

deviated positively from the fitted growth curves (i.e., if

all trees were growing faster in that year than expected

from their age-related growth and having accounted for

growth differences among sites), then ak would be posi-

tive, with the magnitude of ak related to the magnitude of

the anomalies averaged across both series and sites, and

taking account of differences in sample sizes. The ak are

thus a measure of the average deviation from the fitted

growth curves in any year k and are what we are inter-

ested in: they estimate the non-age-related growth signal

common to all series, which may reflect a common cli-

matic signal. The third component, eijk is the remaining

ring-width variation in the ith series from the jth site in

the kth year that was unexplained by diameter, age, site or

calendar year, which we modelled as normally distributed

with mean 0 and variance r2.

We also used a hierarchical approach to model the

parameters b0ij, b1ij and b2ij, assuming that the parameter

values for individual series were derived from a larger

population of possible parameter values that were modelled

as drawn from a multivariate normal distribution with

means and variances estimated from the data. Hence, we

fitted the following model:

log rwijk

� �¼ b0ij þ b1ij log diamijk

� �þ b2ij ageijk

� �þ cjk

þ ak þ eijk

with:

cjk�N 0; r2c

� �

ak �N 0; r2a

� �

eijk �N 0; r2� �

b0ij

b1ij

b2ij

0

@

1

A�N

lb0

lb1

lb2

0

@

1

A;r2

b0q1rb0

rb1q2rb1

rb2

q1rb0rb1

r2b1

q3rb0rb2

q2rb1rb2

q3rb0rb2

r2b2

0

B@

1

CA

0

B@

1

CA

This models tree ring widths as a function of cambial age

and tree diameter, with the equation’s parameters (b0ij, b1ij,

b2ij) varying by series subject to the constraint that they

follow a multivariate normal distribution with overall mean

lb0; lb1

; lb2

� �and covariance matrix having correlation

parameters q1; q2; q3 as above.

We fitted the above model using maximum likelihood as

implemented in the function ‘lmer’ in the ‘lme4’ library in

the R statistical package (http://www.R-project.org). Esti-

mates of ak were extracted using the function ‘ranef’.

These were back-transformed from the log to the original

scale and these back-transformed values were used as our

ring-width index of the common non-age-related growth

signal in each year.

As a measure of chronology reliability, we calculated

the expressed population signal (EPS; Wigley et al. 1984)

using a 50-year window with 25-year overlap (Fig. 1d).

The entire chronology spanned the period 1338–1999 but

EPS was less than 0.85 prior to about 1450 indicating that

the chronology was less reliable prior to this date. We

therefore used the ring-width index values for the period

1450–1999 in our analyses.

2.2 Climate response and New Zealand temperature

reconstruction

We used instrumental mean annual and monthly climate

data averaged across NZ with respect to the 1961–1990

mean (using temperature data updated from Folland and

Salinger (1995)) to examine the climate response of this

1432 R. P. Duncan et al.: Non-uniform interhemispheric temperature trends

123

chronology. We correlated the ring width index with mean

monthly temperatures for the common period (1853–1999)

and assessed the significance of the correlation coefficients

using 1,000 bootstrap samples, as implemented in the R

library ‘bootRes’.

For comparison with NH reconstructions (see below),

the H. biformis chronology was calibrated against mean

annual temperature averaged across NZ with respect to the

1961–1990 mean. We used a split calibration/verification

approach, dividing the common time period in two (1853–

1925 and 1926–1999), using one period for calibration and

the second for verification, and then reversing the process.

For each period we calculated verification statistics: the

reduction of error (RE) and the coefficient of efficiency

(CE; Cook and Kairiukstis 1990).

Because the application of hierarchical regression

modeling to chronology construction is novel, we com-

pared the performance of this method to the standard

approach of removing the age-related growth trend in each

series using splines, and constructing a chronology by

averaging the resulting indices within and then across-sites.

The chronology constructed using the hierarchical regres-

sion model performed substantially better in temperature

verification (see Supplementary Material), which justified

the use of this approach.

2.3 Northern hemisphere temperature reconstruction

The NZ mean annual temperature reconstruction was

compared with the arithmetic average of nine recent NH

temperature reconstructions (Jones et al. 1998; Mann et al.

1999; Crowley and Lowery 2000; Briffa et al. 2001; Esper

et al. 2002; Huang 2004; Moberg et al. 2005; D’Arrigo

et al. 2006; Hegerl et al. 2006) over the common period

(AD 1450–1995). For the H. bifomis chronology, fitting an

age-related growth curve to each series removed any low-

frequency common growth signal; the ‘‘segment length

curse’’ (Cook et al. 1995). The NH temperature recon-

structions included such low-frequency variation, which

we removed by detrending with a 100-year Gaussian filter

with the ends padded using mean values. The NZ tem-

perature reconstruction was similarly detrended for

consistency. This means that our comparison of NH and

NZ temperature trends considers only variation at less than

centennial scales.

2.4 IPO and AMO comparisons

To compare temperature variations between NH and NZ,

we used the unsmoothed Interdecadal Pacific Oscillation

(IPO) index downloaded from ftp://www.iges.org/pub/

kinter/c20c/IPO.doc, and the unsmoothed Atlantic Multi-

decadal Oscillation index from the Kaplan SST V2

downloaded from http://www.cdc.noaa.gov/Timeseries/

AMO.

We assessed whether the IPO index alone, the AMO

index alone, or an index constructed by summing the two,

best correlated with the temperature difference between

NH and NZ over the common period (after normalising the

two indices to mean 0 and standard deviation 1). To

determine their relative weighting when summing the

indices, we determined the correlation between the NH–NZ

temperature difference and the index:

IPOþ mAMO

for different values of m [ 0.

3 Results

The H. biformis chronology for the period 1450–1999 is

shown in Fig. 2. The ring-width index was significantly

positively correlated with NZ monthly temperatures across

the 12 month period of the current year, with the strongest

correlations during the growing season months (Decem-

ber–February) and again in May towards the end of the

growing season (Fig. 3a). The significant positive correla-

tions across all months suggests that the H. biformis

chronology should capture variations in mean annual

temperature. When calibrated against NZ mean annual

temperature, verification statistics [reduction of error (RE)

and coefficient of efficiency (CE)] were both greater than

zero in each of the verification periods indicating the

chronology is a reliable proxy for NZ mean annual

1500 1600 1700 1800 1900 2000

0.6

1.0

1.4

Year

Rin

g w

idth

inde

x

Fig. 2 The Halocarpusbiformis chronology from 1450

to 1999 (grey line), the

chronology smoothed with a

20-year Gaussian filter (blueline, with the ends padded by

mean values) and the mean of

the series post 1450 (horizontaldashed line). Each year includes

measurements from a minimum

of 22 series from at least 7 sites

R. P. Duncan et al.: Non-uniform interhemispheric temperature trends 1433

123

temperature, with a close match between actual and

reconstructed values (Fig. 3b). The final temperature

reconstruction was done using data for the entire period

1853–1999, for which the ring width-temperature correla-

tion was 0.65.

Since 1900 AD there have been marked departures from

the overall warming trend and these are evident in both the

detrended NZ and mean NH reconstructions as oscillations

about the zero line which match observed temperature

variations (Fig. 4a). In the NH series, the oscillations

coincide with the period of warming from AD 1910–1940

followed by cooling from AD 1940–1975 and then sub-

sequent warming. In NZ, the oscillations since AD 1900

appear to be directly out of phase with the NH, and con-

sistent with the observation that pronounced warming

commenced in the SH around AD 1950 when the NH was

in a cooling phase (Brohan et al. 2006).

To explore the above relationship further, we plotted the

difference between the two detrended series (Fig. 4b).

Because oscillations in the detrended series are present as

deviations around zero, intervals when the series are out of

phase are evident as positive (NH warm while NZ cool) or

negative (NZ warm while NH cool) differences in Fig. 4b.

During the period for which IPO and AMO index data are

available (AD 1871–1995), the reconstructed NH–NZ tem-

perature difference is positively correlated with both the

IPO and AMO indices (r = 0.45 for both), but more

strongly correlated with an index constructed by summing

the two (r = 0.57; Fig. 4c), with the strongest correlation

with the NH–NZ temperature difference occurring when

m = 1 and the two indices were equally weighted (Fig. 5).

Hence, since AD 1900 mean reconstructed temperatures in

the NH and NZ have oscillated out of phase, and the

oscillations are strongly associated with variation in both

the IPO and AMO (instrumental temperatures show the

same pattern; see Supplementary Material).

Comparison of the two series over the last 550 years

suggests that temperature oscillations in NZ and the NH

have been out of phase for extended periods, notably in the

early-mid AD 1500s, mid AD 1600s, early AD 1700–1800 and

0.1

0.3

0.5

Cor

rela

tion

coef

ficie

nt

June July

Augu

stSe

ptem

ber

Oct

ober

Nov

embe

rD

ecem

ber

Janu

ary

Febr

uary

Mar

ch

April

May

(a)

(b)

1850 1900 19502000

-1.5

-0.5

0.5

Year

Tem

pera

ture

ano

mal

y

RE = 0.43

CE = 0.12

RE = 0.5

CE = 0.27

Fig. 3 a Correlation coefficients for the relationship between Halo-carpus biformis ring width index and instrumental monthly mean

climate data averaged across NZ. Coefficients were calculated from

June through to May at the end of the current growing season. 95%

confidence intervals based on 1,000 bootstrap samples are shown as

lines. All coefficients were significantly greater than zero; b Actual

New Zealand mean annual temperature anomaly with respect to the

1961–1990 mean (orange line) and mean annual temperature

anomaly reconstructed from the chronology (blue line). Verification

statistics [reduction of error (RE) and coefficient of efficiency (CE)]

are both greater than zero in the two verification periods (1853–1925

and 1926–1999) indicating the chronology is a reliable proxy for NZ

mean annual temperature. The final calibration was done using data

for the entire period 1853–1999, for which the ring width-temperature

correlation was 0.65

1434 R. P. Duncan et al.: Non-uniform interhemispheric temperature trends

123

since AD 1900 (Fig. 4a, b). Moreover, there is clear evi-

dence of periodicity in these temperature differences con-

sistent with the periodicity observed since AD 1900, which

we have shown is associated with combined variations in

the IPO and AMO. To assess this, we analysed the dif-

ference between the NZ and mean NH temperature

reconstructions using wavelet analysis (Torrence and

Compo 1998). To test the significance of power values in

the wavelet spectrum, we used the areawise test described

in Maraun et al. (2007), which uses Monte Carlo methods

to determine whether areas of high power are significantly

greater than would be expected under Gaussian white

noise. This method is an improvement on previous point-

wise significance tests (Torrence and Compo 1998), which

suffer from the problem of multiple testing leading to

spuriously high significance levels (Maraun et al. 2007).

A wavelet spectrum shows three intervals of significant

periodicity centred around 30–60 years during AD 1500–

1600, around AD 1800 and from AD 1900 onwards (Fig. 6).

These observations imply that the out-of-phase fluctuations

around the recent warming trend in NH and NZ tempera-

tures, with a periodicity centred around 30–60 years, have

precedents in the past, strongly suggesting natural climate

variation as their cause.

4 Discussion and conclusions

New Zealand straddles several major atmospheric and

oceanic boundaries in the southern ocean known to be

sensitive to multidecadal climate variations (Salinger and

Mullan 1999), and recent work shows that comparison of

1500 1600 1700 1800 1900 2000

1500 1600 1700 1800 1900 2000

-1.5

-0.5

0.5

Ano

mal

y

(a)

(b)

(c)

Ano

mal

y di

ffere

nce

-4-2

02

4

NZ w arm

NH w arm

1880 1900 1920 1940 1960 1980

-2-1

01

2

Year AD

Inde

x

Fig. 4 Comparison of the detrended New Zealand and mean northern

hemisphere temperature reconstructions, and their relationship with

the Interdedadal Pacific and Atlantic Multidecadal Oscillations. aVariation in the New Zealand (NZ; blue line), nine northern

hemisphere (NH) mean annual temperature reconstructions (greylines), and the mean of those nine NH reconstructions (red line). The

series were normalised to mean zero and standard deviation one, the

average NH series was obtained by taking the mean of the NH series

available in any given year, and then all series were detrended with a

100-year Gaussian filter (with the ends padded by mean values) to

remove low-frequency variation. The detrended series have been

smoothed with a 20-year Gaussian filter to highlight decadal to

multidecadal level variation. b Difference between the NZ and mean

NH temperature reconstructions (calculated as NH–NZ) after detr-

ending with a 100-year Gaussian filter and normalising each series to

mean zero and standard deviation one (grey line), and the difference

smoothed with a 20-year Gaussian filter (blue line). c Difference

between the NZ and mean NH temperature reconstructions (orangeline) and the sum of the IPO and AMO indices (blue line)

R. P. Duncan et al.: Non-uniform interhemispheric temperature trends 1435

123

temperature sensitive proxies in New Zealand with the

northern hemisphere can provide insights into the nature of

hemispheric climate variations (Denton and Broecker

2008; Schaefer et al. 2009).

At multidecadal scales, our results suggest that internal

variability associated with the effects of the IPO and AMO

may underlie observed hemispheric variation around the

recent warming trend, supporting recent analyses and

model predictions (Parker et al. 2007; Zhang et al. 2007).

While there are three intervals during the last 550 years

where New Zealand temperatures show out-of-phase

oscillations with the northern hemisphere consistent with

an IPO-AMO driver (Fig. 4a), these are interspersed with

intervals of low power at similar periodicity (30–60 years)

in the wavelet spectrum (Fig. 6), notably between

AD 1670–1710 and AD1830–1900. During these intervals

multidecadal temperature oscillations in NZ and the NH do

not appear out-of-phase and tend to be more closely

aligned. This pattern matches that seen in Schaefer et al.

(2009) over longer timescales during the mid- to late

Holocene, where glacier length fluctuations in NZ were

sometimes out-of-phase and sometimes in-phase with the

northern hemisphere.

Hence, while multidecadal oscillations in the IPO and

AMO associated with out-of-phase temperature fluctua-

tions in NZ and the NH are a recurring phenomena, they

do not appear to persist. Such an outcome is consistent

with a dynamic mechanism proposed to explain major

climate shifts (Tsonis et al. 2007), in which oscillations

associated with climate indices (here the IPO and AMO)

become synchronized and reinforce each other, mani-

fested here as strong out-of-phase temperature fluctuations

between the NH and NZ. The synchronous state then

collapses and a new climate state emerges, here with

temperature fluctuations between the NH and NZ more

closely aligned. Such a mechanism might account for

significant power in the wavelet spectrum at periodicities

around 8–20 years, and shorter, throughout the record

(Fig. 5), as well as variations over much longer timescales

(Denton and Broecker 2008).

Regardless of the underlying mechanisms, our results

suggest that large-scale multidecadal fluctuations around

the warming trend since AD 1900 may be partly driven by

internal climate oscillations associated with the IPO and

AMO, and manifest as out-of-phase hemispheric fluctua-

tions. Incorporating this variability into climate models

should improve decadal to multidecadal forecasts of future

climate change (Smith et al. 2007; Keenlyside et al. 2008).

Such out-of-phase fluctuations, however, appear as a

recurring but transient phenomena; our reconstruction

reveals that past states with out-of-phase oscillations hav-

ing periodicity similar to that observed since AD 1900

persisted for c. 100 years before apparently shifting to a

different state. This adds considerable uncertainty to dec-

adal forecasts if, as the long-term record suggests, we may

be approaching a state-shift where the processes that have

driven oscillations around the warming trend over the last

century are set to change. Overlaid on this are observations

that regional drivers may also contribute to hemispheric

fluctuations at much longer timescales (Denton and

Broecker 2008; Schaefer et al. 2009). Future work is

required to develop robust, long-term proxies of southern

hemisphere temperatures to test the generality of hemi-

spheric variations suggested by the New Zealand data.

Year AD

Per

iod

(yea

rs)

1500 1600 1700 1800 1900

24

816

3264

128

Fig. 6 Wavelet power spectrum for the difference between the NZ

and mean NH temperature reconstructions. The colours code for

power values from low (blue) to high (red). Solid lines enclose

regions of significant (P \ 0.05) power based on an areawise test

using Monte Carlo simulation (Maraun et al. 2007). The dashed lineis the cone of influence indicating the region outside of which

estimates are based on partial waves

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.46

0.50

0.54

m

Cor

rela

tion

Fig. 5 Correlation between the

NH–NZ temperature difference

(from Fig. 1b) and the index

IPO ? mAMO, for different

values of m

1436 R. P. Duncan et al.: Non-uniform interhemispheric temperature trends

123

Acknowledgments Jim Renwick kindly provided the updated New

Zealand temperature data. RPD and MSM were funded by the

Foundation for Research, Science & Technology under contract

CO9X0502 (Ecosystem Resilience OBI), and CSMT acknowledges

the Australian Greenhouse Office and the Australian Research

Council (ARC LX0776040) for their support. This work forms a

contribution to the PAGES Aus2k working group. We thank two

referees for very helpful comments on the manuscript.

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