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Climate
of the Past
A unified proxy for ENSO and PDO variability since 1650
S. McGregor, A. Timmermann, and O. Timm
IPRC, SOEST, University of Hawaii at Manoa, Hawaii, USA
Received: 24 August 2009 – Published in Clim. Past Discuss.: 23 September 2009
Revised: 1 December 2009 – Accepted: 6 December 2009 – Published: 5 January 2010
Abstract. In this manuscript we have attempted to consol-
idate the common signal in previously defined proxy recon-
structions of the El Ni
˜
no-Southern Oscillation into one indi-
vidual proxy titled the Unified ENSO Proxy (UEP). While
correlating well with the majority of input reconstructions,
the UEP provides better representation of observed indices
of ENSO, discrete ENSO events and documented historical
chronologies of ENSO than any of these input ENSO recon-
structions. Further to this, the UEP also provides a means
to reconstruct the PDO/IPO multi-decadal variability of the
Pacific Ocean as the low-pass filtered UEP displays multi-
decadal variability that is consistent with the 20th century
variability of the PDO and IPO. The UEP is then used to
describe changes in ENSO variability which have occurred
since 1650 focusing on changes in ENSOs variance, multi-
year ENSO events, PDO-like multi-decadal variability and
the effects of volcanic and solar forcing on ENSO. We find
that multi-year El Ni
˜
no events similar to the 1990–1995 event
have occurred several times over the last 3 1/2 centuries.
Consistent with earlier studies we find that volcanic forcing
can induce a statistically significant change in the mean state
of ENSO in the year of the eruption and a doubling of the
probability of an El Ni
˜
no (La Ni
˜
na) event occurring in the
year of (three years after) the eruption.
1 Introduction
The El Ni
˜
no-Southern Oscillation (ENSO) is a coupledtropi-
cal Pacific ocean-atmosphere phenomenon which is the most
dominant global source of interannual climate variability.
ENSO is an oscillation characterized by marked changes
in eastern equatorial Pacific sea surface temperature (SST),
Correspondence to: S. McGregor
(shaynemc@hawaii.edu)
known as El Ni
˜
no, and a related large scale seesaw in atmo-
spheric sea level pressure between the Australia-Indonesian
region and the south-central tropical Pacific known as the
Southern Oscillation. ENSO influences extreme weather
events such as drought, flooding, bushfires and tropical cy-
clone activity across vast regions of the globe (Chan, 1985;
Nicholls, 1985; Power et al., 1999). While the importance of
its climatic impacts are relatively well known, many charac-
teristics of the long term changes in ENSO frequency, magni-
tude and duration remain unknown. Further to this, whether
ENSO variability has changed due to the already observed
anthropogenically-induced climate change and how future
projected changes will further influence ENSO are largely
unresolvedquestions (Federovand Philander, 2000; van Old-
enborgh et al., 2005; Merryfield, 2006).
For example, over the last few decades ENSO variabil-
ity has appeared to shift toward a more El Ni
˜
no-like state
(Federov and Philander, 2000) where the 1980s and 1990s
contained the two largest observed El Ni
˜
no events on record
along with a prolonged El Ni
˜
no-like warming in the trop-
ical Pacific Ocean that persisted for approximately 5-yrs
in the early 1990s. Many studies have tried to ascertain
the cause of these unusual events focusing on the roles
of anthropogenically-induced climate change and natural
decadal or ENSO variability (Latif et al., 1997; Power and
Smith, 2007). At this stage it can not be ruled out with confi-
dence that the recent changes of ENSO variability are a man-
ifestation of natural variability (Timmermann, 1999).
However, a major difficulty in separating the anthro-
pogenic signal from natural variability of the climate system
is that the instrumental record coversaperiodoflessthan150
years which is much too brief to properly address this funda-
mental question. Ideally to accurately ascertain the roles of
natural and anthropogenically-induced variability of ENSO,
hundreds to possibly even thousands of years of SST records
are needed. As such, multi-century paleo climate reconstruc-
tions derived from annually resolved tree-ring, ice-core and
Published by Copernicus Publications on behalf of the European Geosciences Union.
2 S. McGregor et al.: The unified ENSO proxy
1650 1700 1750 1800 1850 1900 1950
−3
−2
−1
0
1
2
3
Normalised ENSO reconstructions
Year
a)
1650 1700 1750 1800 1850 1900 1950
0
1
2
Std deviation
Year
b)
Fig. 1. Time series of (a) the 10 reconstructions (Table 1) of ENSO variability (gray) and the mean of these reconstructions (black). (b)
Standard deviation of the 10 reconstructions.
coral records stand as the only way to assess the long term
variability of ENSO.
There are currently numerous well crafted reconstructions
of ENSO variability which all use a variety of current and
paleo proxy data sources and a variety of methods to derive
the indices (see discussion in Sect. 2) (Dunbar et al., 1994;
Stahle et al., 1998; Cook, 2000; Mann et al., 2000; Evans
et al., 2001, 2002; Cobb et al., 2003; Cook et al., 2008; Bra-
ganza et al., 2009). Each of these individual reconstructions
correlates reasonably well with observations of ENSO vari-
ations during the instrumental period. However, while all
represent the same signal there is a great deal of variability
amongst the proxies (Fig. 1) which acts to reduce confidence
in the individual proxies. For example, when looking at the
normalized time series of 10 commonly used ENSO proxies
(Dunbar et al., 1994; Stahle et al., 1998; Cook, 2000; Mann
et al., 2000; Evans et al., 2001, 2002; Cobb et al., 2003; Cook
et al., 2008; Braganza et al., 2009), we find that the standard
deviation between the proxies at each time step is almost as
large as the standard deviation of each index (Fig. 1). This
variability amongst the proxy ENSO signals could represent
an un-ENSO related climatic signal, an underlying biologi-
cal signal, inaccurate proxy dating, or even ENSO itself. Ev-
ery ENSO event is different and each event creates a slightly
different spatial teleconnection pattern (Trenberth and Stepa-
niak, 2001). Therefore proxies derived from different regions
could be expected to have slightly different signals of ENSO
variability.
Here we attempt to consolidate the information contained
in these 10 ENSO reconstructions to come up with a unified
ENSO proxy (UEP) that captures the joint features of these
reconstructions. This paper is organized as follows. Sect. 2
provides a description of the ENSO proxies used to derive
the UEP in this study. Section 3 details the methods used
to derive the UEP, while in section 4 the new ENSO proxy
is validated against observations and historical documentary
chronologies of ENSO. In section 5 we then discuss the vari-
ability of ENSO since 1650 and try to put into context the
variability of the 20th century. A discussion and conclusions
from the study are then presented in Sect. 6.
2 ENSO proxy data
The criteria used for the selection of proxy records used in
this study were that theyneededto (i) have at least annual res-
olution, (ii) extend back until at least the year 1800, and (iii)
either be described in the literature as representing ENSO
variability or be located in ENSO’s prime center of action,
the eastern-central tropical Pacific. Using these criteria we
found 10 different proxies of ENSO variability. They are the
proxies of
Dunbar et al. (1994); Stahle et al. (1998); Cook
(2000); Mann et al. (2000); Evans et al. (2001, 2002); Cobb
et al. (2003); Cook et al. (2008); Braganza et al. (2009) (see
Table 1).
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S. McGregor et al.: The unified ENSO proxy 3
Fig. 2. The location and type of source proxy used in 9 of the 10 original ENSO proxies. See Table 1 for identification the proxy reconstruc-
tion corresponding to the proxy number displayed here.
Each of these ENSO proxies uses a variety of input data
sources and methods to derive their representation of past
ENSO variability. For example, when considering the proxy
records used as inputs for these 10 proxies, at one end of
the spectrum we have the proxy records of Dunbar et al.
(1994) and Cobb et al. (2003) which both source data from
corals based in a single region which is directly influenced
by ENSO. Whereas, at the other end of the spectrum we
have studies like that of Mann et al. (2000) who use a multi-
proxy network of diverse proxy indicators located in tropical-
subtropical regions that are influenced directly by ENSO or
through its teleconnections. It is just as diverse when consid-
ering the methods used to derive these proxies from the orig-
inal input proxy network. For example, the studies which
only include data from one specific source region gener-
ally just splice each of the individual records together (Cobb
et al., 2003). Whilst studies that use a multi-proxy network
generally use a mathematical analysis technique (such as a
Principal Component Analysis) to either, identify the com-
mon signal within the input proxy network (Braganza et al.,
2009), or todecompose the observed data into their dominant
spatiotemporal eigenmodes (Mann et al., 2000; Evans et al.,
2002). In the latter case, the proxy data are then regressed
against the associated times series of the leading eigenmodes
during an overlapping period. In such way the regressed
proxy data time series and the associated eigenvectors pro-
vide an estimate the spatiotemporal variability prior to the
observational period.
The data redundancy across the 10 input networks used
for the generation of these proxies is relatively small. For
instance, there were 155 proxies used in total in the 9 of the
10 input networks and of these, less than 1/4 were used in
Table 1. ENSO proxies employed in this work.
Proxy Start End Reference Source Proxy
number Year Year Location
1 1300 1978 Cook (2008) North America
2 1408 1978 Cook (2000) North America
3 1525 1982 Braganza et al. (2009) Pacific Basin
4 1590 1990 Evans et al. (2001) America
5 1607 1981 Dunbar et al. (1994) Eastern Equatoiral Pacific
6 1635 1998 Cobb et al. (2003) Central Equatorial Pacific
7 1650 1990 Mann et al. (2000) Near Global (tropics)
8 1706 1997 Stahle et al. (1998) Pacific Basin
9 1727 1982 Braganza et al. (2009) Pacific Basin
10 1800 1990 Evans et al. (2002) Indo-Pacific Basin
more than one of the proxy input networks. Further to this,
it is clear looking at the geographic distribution and type of
the input proxies used in these 9 selected ENSO proxies that
the selected network is derived from a truly diverse multi-
proxy input network (Table 1, Fig. 2). Whilst details about
the source data of Cook et al. (2008) are not published as
yet, the new proxy network contains many additional data
which results in relatively small data redundancy (approxi-
mately 25%) between the new tree ring derived reconstruc-
tion of Cook et al. (2008) and the previously published tree
ring derived reconstruction of Cook (2000) (Cook, personal
communication, 2009). Considering that the diversity and
distribution of the network has been proposed to reduce the
effects of biases and weaknesses in the individual indicators,
we expect that the use of this network of ENSO proxies will
allow us to more accurately capture the climatic signal of
ENSO variability. However, we must note that the North
American region could be overrepresented as 7 out of the 10
input ENSO reconstructions used in this study utilise North
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4 S. McGregor et al.: The unified ENSO proxy
1650 1700 1750 1800 1850 1900 1950
−3
−2
−1
0
1
2
3
Proxy ENSO index
Year
Fig. 3. The unified ENSO proxy (UEP) for the period 1650–1978.
Table 2. % variance explained by the first four principal compo-
nents along with the normalized eigenvectors for each proxy.
Proxy PC1 PC2 PC3 PC4
Variance 52.5% 13.9% 9.7% 7.1%
Proxy 1 12.3% 9.0% 7.6% 14.5%
Proxy 2 12.3% 7.1% 4.1% 17.5%
Proxy 3 12.4% 10.5% 2.9% 8.2%
Proxy 4 10.1% 3.2% 2.1% 21.9%
Proxy 5 0.0% 14.0% 46.4% 2.6%
Proxy 6 7.5% 25.5% 12.0% 2.3%
Proxy 7 11.3% 3.4% 7.8% 5.7%
Proxy 8 12.9% 1.8% 1.7% 8.5%
Proxy 9 13.1% 4.4% 2.9% 11.6%
Proxy 10 7.8% 21.0% 12.3% 7.0%
American tree ring data in their ENSO reconstruction deriva-
tion. This may result in a common mode that is more highly
weighted to this ENSO teleconnection region.
3 Methods
The various forms of analysis used to generate ENSO prox-
ies from the original multi-proxy network were carried out
to isolate the ENSO variability within the network by re-
moving the effects of climatic noise (i.e., local climatic and
other large scale variability uncorrelated to ENSO) and non-
climatic factors (i.e., biological processes). However, due
to the large variability between these indices we believe that
each of the proxies utilized incorporates, to varying degrees,
a noise component that is not related to ENSO (cf. Fig. 1b).
Thus, to decompose this new multi-variable ENSO proxy
network into a single leading mode of covariability, we carry
out a Principal Component Analysis (PCA). The PCA anal-
ysis removes variability that is not coherent across the in-
put network by essentially making coherent signals within
the network of ENSO proxies leading order modes while
variability that is not coherent across the network generally
makes up the lower order modes. As such, we expect the first
principal component (PC) of the PCA analysis to represent
the common signal in each of the selected proxies, ENSO.
Furthermore, the PCA also gives us eigenvectors which indi-
cate how important each original input ENSO proxy is in the
resultant principal components.
To this end, the PCA analysis was carried out on the ENSO
proxy network over the period 1650-1977. Normally, the
output of a PCA would not be calibrated to the observations,
however, here as several of the input proxies are calibrated
the resulting ENSO proxy (the UEP) is not completely un-
calibrated to the twentieth century observations. The first PC
of the PCA analysis displayed in Fig. 3 accounts for approx-
imately 52% of the ENSO proxy covariability (see Table 2).
PC1’s associated eigenvectors, displayed in Table 1, indicate
that the weighting is relative equally distributed across 9 of
the 10 proxies used with the only outlier being the coral de-
rived proxy of Dunbar et al. (1994) which has a weighting of
approximately 0. The remaining PCs account for 48% of the
ENSO proxy covariability, where the second, third and forth
PCs accounting for approximately 14%, 10% and 7% of the
variance, respectively.
The agreement between the first PC and the original input
proxies is assessed by calculating the correlation coefficients
between the time series. As expected when considering the
eigenvectors of PC1, 9 of the original 10 input ENSO proxies
have extremely good correlations with the PC1 that are sta-
tistically significant above the 99% level (Table 3). Further,
we find that PC1 correlates extremely well with the mean of
the 10 originalinput proxies where the correlation coefficient
is 0.98. We note here however, that although the PC1 corre-
lates well with mean of the input proxies there are some sig-
nificant differences in the magnitude of the variability that
is most prominent in the years surrounding 1700 and 1900
(figure not shown). Thus, it appears that the first PC captures
the common signal in the input proxies. As such, we will
hereafter call the first PC of this analysis the “Unified ENSO
Proxy” (UEP).
To test the robustness of the UEP to the choice and num-
ber of multi-proxy based ENSO indices used, and the robust-
ness of the other higher order modes, we carry out a series
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S. McGregor et al.: The unified ENSO proxy 5
Table 3. Correlation coefficients and root mean squared error (RMSE), in brackets, of the Unified ENSO proxy (UEP) and the original
proxy network with the UEP and observations during the overlapping period. Sources: SOI, Ropelewski and Halpert (1987); Kn34, Kaplan
et al. (1998); Hn34, Rayner et al. (2003); and Bn34, Bunge and Clarke (2009). Statistical significance of greater than the 99% level in the
correlation coefficients is indicated by bold font.
Correlation (RMSE) UEP SOI Kn34 Hn34 Bn34
UEP 1.0 (0.0) −0.81 (0.58) 0.81 (0.58) 0.82 (0.57) 0.81 (0.59)
Proxy 1 0.84 (0.54) −0.74 (0.67) 0.70 (0.71) 0.73 (0.68) 0.71 (0.69)
Proxy 2 0.84 (0.54) −0.73 (0.68) 0.71 (0.70) 0.72 (0.69) 0.67 (0.74)
Proxy 3 0.85 (0.52) 0.62 (0.78) −0.64 (0.77) −0.65 (0.76) 0.65 (0.75)
Proxy 4 0.68 (0.73) 0.57 (0.82) −0.60 (0.80) −0.61 (0.79) 0.58 (0.81)
Proxy 5 0.00 (0.99) 0.11 (0.99) −0.02 (1.0) −0.08 (0.99) 0.06 (0.99)
Proxy 6 0.61 (0.79) −0.62 (0.79) 0.67 (0.74) 0.65 (0.75) 0.67 (0.74)
Proxy 7 0.76 (0.64) 0.74 (0.67) −0.74 (0.67) −0.76 (0.64) 0.77 (0.64)
Proxy 8 0.86 (0.51) 0.75 (0.65) −0.71 (0.70) −0.69 (0.72) 0.69 (0.72)
Proxy 9 0.87 (0.49) 0.70 (0.71) −0.69 (0.72) 0.70 (0.71) 0.69 (0.72)
Proxy 10 0.49 (0.87) 0.58 (0.81) −0.65 (0.76) −0.69 (0.73) 0.67 (0.73)
of sensitivity tests. These tests are specifically designed to
answer the questions: if only a subset of the available ENSO
proxies were selected would the output of the PCA change?
how much does it matter which proxies are selected? and
does it make a difference if no calibrated input proxies are
used?
Starting with only testing the robustness/sensitivity of the
first principal component we generate subsets of ENSO prox-
ies, each of the new proxy subsets are combinations contain-
ing 5 of the 10 original ENSO proxies. In total we have 252
subset networks which cover all possible combinations of 5
proxies from the original ENSO proxy network (hereafter re-
ferred to as the 10choose5 proxy network). We now carry out
a PCA on each proxy subset within the 10choose5 proxy net-
works, giving 252 first principal components (one for each
subset). We then seek to identify what the dominant signal is
among these 252 normalized first PCs. There are three ways
we can identify the dominant signal in this network. Using
the first method we would carry out a PCA to identify the
dominant mode, while using the second (third) method we
would simply calculate the mean (median) of the 252 first
PCs at each time point. Each of these methods identifies the
same dominant signal in the first PCs with correlations be-
tween each method having coefficients >0.999. Further, this
dominant signal has a correlation coefficient of >0.998 when
compared with the UEP.
Now to assess the robustness of this new ENSO proxy we
use the signal to noise ratio (SNR) of the 10choose5 proxy
network first PCs, whereby the higher the ratio the less dom-
inant the noise component becomes. The SNR of each of the
252 first modes is defined by the equation:
SNR= (
σ
signal
σ
noise
)
2
, (1)
where σ is the standard deviation, the signal is defined as
the median of the 252 first modes at each time point (which
is correlated at 0.998 when compared with the new ENSO
proxy) and the noise component of each of the 252 first PCs
is defined as the component remaining when the signal is
subtracted from the each of the 252 first PCs. We would ex-
pect that if the dominant mode of covariability identified by
our initial analysis was not robust the signal to noise ratio
(SNR) would be low. Meaning, which of the original ENSO
proxies we select to include in our analysis is extremely im-
portant as the noise can easily dominate the signal of our
newly defined ENSO proxy. On the other hand, if proxy
choice is not important the SNR should be large, such that
any choice of proxies should identify a very similar domi-
nant mode
We find that the UEP signal (the new ENSO proxy) dom-
inates the noise component (meaning a SNR>1) regardless
of which 5 of the original ENSO proxies we select for our
analysis. The mean SNR of all of the 252 first principal com-
ponents is 11.83 and the minimum and maximum SNRs are
2.55 and 26.61 respectively. Further to this, the higher the
number of proxies selected from the original network (e.g.,
if we generate a 10choose6 or 10choose7 proxy network and
carryout the same analysis described above), the larger the
SNR becomes and the more dominant the UEP signal be-
comes. For the 10choose6 (10choose7) case the mean SNR
becomes 17.95 (28.35) while the minimum and maximum
SNR become 4.89 (9.90) and 43.82 (66.56). Therefore, the
UEP is robust regardless of the number of proxies selected
(assuming it is 5 or more) and what proxies are selected from
the original network (i.e., whether proxies are included that
were calibrated to 20th century observations).
On the other hand, if the same set of sensitivity tests de-
scribed abovearecarriedout for PC2 then the averageSNRis
much lower(meanSNRof1.55)and, depending on the selec-
tion of original proxies used, the amplitude of the noise can
become larger than that of the signal (meaning a SNR<1).
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6 S. McGregor et al.: The unified ENSO proxy
1880 1900 1920 1940 1960
−
3
−
2
−
1
0
1
2
3
Year
a)
1880 1900 1920 1940 1960
−3
−2
−1
0
1
2
3
Year
b)
1880 1900 1920 1940 1960
−
3
−
2
−
1
0
1
2
3
Year
c)
1880 1900 1920 1940 1960
−3
−2
−1
0
1
2
3
Year
d)
Fig. 4. The time series of observed (a) SOI, (b) Kn34 SSTA, (c) Hn34 SSTA, and (d) Bn34 SSTA are represented by the solid black line
while overlaid as a gray dashed line is the UEP.
Since this result holds regardless of the number of proxies
selected from the original proxy network, the second princi-
pal component is not robust and is easily contaminated with
noise.
4 Unified ENSO proxy
In this section we compare the time series of the newly de-
veloped unified ENSO proxy (UEP) to instrumental ENSO
indices over the 20th century and documented historic ENSO
chronologies. The comparisons are done using different
methods which include temporal correlation and skill in
the ability of the proxy to simulate threshold based ENSO
events.
4.1 Comparison with the instrumental record
Normalized anomalies of observed annual mean (calculated
over the months July–June) values of the Southern Oscilla-
tion Index (SOI) for the period 1876–1977 were obtained
from Ropelewski and Halpert (1987). While SST anomaly
(SSTA) data for the Ni
˜
no 3.4 region region (5
◦
S–5
◦
N,
120
◦
W–170
◦
W) for the period 1856–1977, 1870–1977 and
1873–1977 was obtained respectively from Kaplan et al.
(1998), Rayner et al. (2003) and Bunge and Clarke (2009).
These three Ni
˜
no 3.4 region SSTA time series will hereafter
be referred to as Kn34, Hn34 and Bn34.
The correlation coefficients (R) between the UEP andSOI,
Kn34, Hn34 and Bn34 are −0.81, 0.81, 0.82 and 0.81 respec-
tively and each of these coefficients is statistically significant
above the 99% confidence level (see Table 3 and Fig. 4). We
note that the statistical significance of all correlation coeffi-
cients presented in the study take into account serial (auto-)
correlation in the series based on the reduced effective num-
ber of degrees of freedom outlined by Davis (1976). These
correlation coefficients are larger than any of the correlation
coefficients generated when comparing the 10 original input
proxies versus observed indices of ENSO. Furthermore, us-
ing a least squares linear regression model for each of the
original ENSO proxies along with the new ENSO proxy we
find the the new ENSO proxy has the smallest root mean
square error (Table 3). Therefore, this confirms that the PCA
analysis has acted to isolate the common ENSO related com-
ponent of each of the original input proxies into PC1, our
new ENSO proxy.
Using the RMSE, which is an estimate for the standard
deviation of the scatter about the regression line, we cal-
culate the two sided 90% confidence interval for output of
the regression model (von Storch and Zwiers, 2000) (page
154). This confidence interval, which is slightly less than
one standard deviation, gives an approximate range in which
the true unobserved ENSO index value is expected with 90%
chance. Note, we neglect the uncertainty associated with
the estimate of the slope which is small compared with the
RMSE. Further to this, there are nonlinearities within cer-
tain sub-samples of ENSO timeseries (i.e., periods in the
observational record in which ENSO indices display signifi-
cant skewness). During these periods outliers may therefore
occur more frequently than expressed by the nominal confi-
dence level. With the above caveats in mind, the confidence
levels are useful for the identification of accurate thresholds
for the detection of discrete ENSO events. For example, if
we want to make sure that an identified El Ni
˜
no year was
not associated with a true La Ni
˜
na event with 95% confi-
dence, one must choose a threshold level such that the lower
confidence range around the reconstructed ENSO estimate is
above the La Ni
˜
na event threshold. In the case of ordinary
linear regression this threshold level can be found in Fig.
5 where the lower confidence limit intersects the horizontal
line marking the La Ni
˜
na threshold. Given that the confi-
dence interval for our regression model is approximately one
standard deviation, a threshold level for ENSO events of 0.5
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S. McGregor et al.: The unified ENSO proxy 7
−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
SOI
UEP
a)
−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
Kn34
UEP
b)
−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
Hn34
UEP
c)
−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
Bn34
UEP
d)
Fig. 5. The least squares linear regression line (shown in black) between the UEP and the (a) SOI, (b) Kn34 SSTA, (c) Hn34 SSTA, and (d)
Bn34 SSTA, while the gray lines indicate the 95% confidence interval.
Table 4. Direct correspondence of El Ni
˜
no (La Ni
˜
na) events between the unified ENSO proxy (UEP) and observations. Observed sources:
SOI, Ropelewski and Halpert (1987); Kn34, Kaplan et al. (1998); Hn34, Rayner et al. (2003); Bn34, Bunge and Clarke (2009).
Correspondence SOI Kn34 Hn34 Bn34
Obs hits 74.2% (75.0%) 80.6% (74.2%) 75.8% (70.0%) 76.7% (70.6%)
Obs misses 25.8% (25.0%) 19.4% (25.8%) 24.2% (30.0%) 23.3% (29.4%)
UEP false events 20.6% (29.4%) 13.8% (32.3%) 13.8% (38.2%) 20.7% (29.4%)
standard deviations above or below the mean satisfies this
criteria. This threshold value is roughly consistent with the
Ni
˜
no 3.4 region SSTA threshold used for the definition of
ENSO events by NOAA (which is 0.5
◦
C above or below the
mean state, calculated from the period 1971–2000, where the
standard deviation of observed Ni
˜
no 3.4 region SSTA for the
period 1971–2000 is 0.93
◦
C).
The accuracy of the UEPs ability to capture discrete El
Ni
˜
no and La Ni
˜
na events in the period 1878–1977 is then as-
sessed. Using this 0.5standard deviationthreshold for ENSO
events means that approximately
1
3
of all years are classified
as either El Ni
˜
no, La Ni
˜
na or Neutral. Looking at the corre-
spondence between the UEP and observations we find that:
(i) of the 31 (32) El Ni
˜
no (La Ni
˜
na) events identified in the
SOI between 1878 and 1977 the UEP correctly identifies 23
(24); (ii) of the 31 (31) El Ni
˜
no (La Ni
˜
na) events identified
in Kn34 between 1878 and 1977 the UEP correctly identifies
25 (23); (iii) of the 33 (30) El Ni
˜
no (La Ni
˜
na) events iden-
tified in Hn34 between 1878 and 1977 the UEP registers 25
(21) of these; and (iv) of the 30 (34) El Ni
˜
no (La Ni
˜
na) events
identified in Bn34 between 1878 and 1977 the UEP registers
23 (24) of these (see Table 4 for more details). Hence, ap-
proximately 77% of the El Ni
˜
no events and 72% of La Ni
˜
na
events during the period 1878–1977are captured by the UEP.
Looking at the false alarm rate of the new ENSO proxy we
find that there were: (i) 6 (10) false El Ni
˜
no (La Ni
˜
na) alarms
raised that were not identified in the SOI; (ii) 4 (11) false
El Ni
˜
no (La Ni
˜
na) alarms raised that were not identified in
Kn34; (iii) 4 (13) false El Ni
˜
no (La Ni
˜
na) alarms raised that
were not identified in Hn34; and (iv) 6 (10) false El Ni
˜
no (La
Ni
˜
na) alarms raised that were not identified in Bn34. Consis-
tent with other studies (e.g., Braganza et al. (2009)) the UEP
showshigherskillin reproducing warm (El Ni
˜
no) events than
the opposite phase.
Comparing the accuracy of the UEPs ability to capture dis-
crete ENSO events to the event capture of the 10 original
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8 S. McGregor et al.: The unified ENSO proxy
1650 1700 1750 1800
Garcia08
Ortlieb00
Gergis09
Quinn92
UEP
Year
1850 1900 1950
Garcia08
Ortlieb00
Gergis09
Quinn92
UEP
Year
Fig. 6. Chronologies of El Ni
˜
no in the 1650–1978 period where vertical bars indicate the year
0
of the El Ni
˜
no event. Here the row labeled
UEP indicates El Ni
˜
no events identified in the UEP by the
1
2
standard deviation threshold, while QN92 refers to Quinn and Neal (1992),
Orlieb00 refers to Ortlieb (2000), Garcia08 refers to Garcia-Herrera et al. (2008) and Gergis09 refers to Gergis and Fowler (2009).
proxies (using the same 0.5 standard deviation threshold to
define El Ni
˜
no and La Ni
˜
na years), we find that the UEP cap-
tures more El Ni
˜
no events in the observational period and
has a lower false alarm rate than any of the original prox-
ies. In regards to the La Ni
˜
na event capture, we find that
the UEP again captures more events than any of the 10 origi-
nal proxies. However, while the false alarm rate is relatively
low at 16%, the false alarm rates of the Stahle et al. (1998),
Mann et al. (2000) and Cook et al. (2008) ENSO proxies are
slightly lower at 10%, 14% and 12%.
4.2 Comparison with historic ENSO chronologies
In this section we continue our assessment of the accu-
racy of the UEPs ability to capture discrete El Ni
˜
no and La
Ni
˜
na events. However, here instead of assessing the UEP
against instrumental observations, we will be assessing the
correspondence between the UEP and documented histori-
cal chronologies of El Ni
˜
no (Quinn and Neal, 1992; Ortlieb,
2000; Garcia-Herrera et al., 2008; Gergis and Fowler, 2009)
and La Ni
˜
na (Gergis and Fowler, 2009) events.
These historic chronologies of ENSO namely utilize doc-
umentary sources of meteorological and climatic extremes
from regions with weather conditions that are normally heav-
ily influenced by ENSO events to provide a mainly binary
type representation of ENSO variability. The most widely
referenced of these chronologies is the El Ni
˜
no chronology of
Quinn and Neal (1992). This chronology, beginning in 1520
and ending in 1990, records El Ni
˜
no years charactorized by
unusual climatic events in the South American region. The
Quinn and Neal (1992) chronology has since been revised
by Ortlieb (2000) to try and account for the ambiguity intro-
duced into the Quinn record by using secondary documen-
tary sources and climatic events in regions where weather is
only weakly related to El Ni
˜
no. This revised work of Ortlieb
(2000) covers the period 1520 through to and including the
year 1900. The more recent documented historical El Ni
˜
no
chronology of Garcia-Herrera et al. (2008), for the period
1550–1900, is based primarily on documentary records in
Northern Peru. The final ENSO chronology used for verifica-
tion in this study is that of Gergis and Fowler (2009). While
the above chronologies all primarily used documentary evi-
dence to assemble their chronologies, the study of Gergisand
Fowler (2009) constructs a chronology of both El Ni
˜
no and
La Ni
˜
na events using a combination of Paleoclimatic data
and chronologies of ENSO variability. We note here that in
cases when magnitude information is also included as part of
the ENSO chronology, this extra information is disregarded
and the information provided is simply used as binary type
information.
Looking at the direct correspondence between the UEP
and chronologies of Quinn and Neal (1992) and Gergis and
Fowler (2009) we find that of the 101 El Ni
˜
no events iden-
tified in the UEP between 1650–1977, approximately 51%
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S. McGregor et al.: The unified ENSO proxy 9
Table 5. Direct and quasi-direct (in parentheses) correspondence between the unified ENSO proxy (UEP) and chronologies of El Ni
˜
no
where QN92 = Quinn and Neal (1992), Orlieb00 = Ortlieb (2000), Garcia08 = Garcia-Herrera et al. (2008) and Gergis09 = Gergis and
Fowler (2009). Quasi-direct correspondence counts direct correspondence along with events in the chronology that lag 1-yr behind the UEP
El Ni
˜
no event.
Correspondence QN92 Ortlieb00 Garcia08 Gergis09
Obs hits 51.1% (75.5%) 29.0% (46.4%) 27.5% (47.8%) 69.1% (91.5%)
Obs misses 49.9% (24.5%) 71.0% (53.6%) 72.5% (52.2%) 30.9 (8.5%)
(69%) are registered in the Quinn and Neal (1992) (Gergis
and Fowler, 2009) chronology (Fig. 6 and Table 5). Also, of
the 110 La Ni
˜
na events identified in the UEP, 57.3% are reg-
istered in the chronology of Gergis and Fowler (2009). As-
sessing the direct correspondence between the UEP and the
El Ni
˜
no chronologies of Ortlieb (2000) and Garcia-Herrera
et al. (2008), over the period 1650–1900, we find that of the
75 El Ni
˜
no events registered in the UEP, approximately 29%
and 28% are respectively registered in the Ortlieb (2000) and
Garcia-Herrera et al. (2008) chronologies (Fig. 6 and Ta-
ble 5). To assess the statistical significance of this corre-
spondence we used a Monte Carlo type approach whereby
we calculated 1000 Fourier based surrogates of the newly
defined UEP using the method described by Theiler et al.
(1992). These surrogates share the same mean, variance and
power spectrum as the UEP, however, their phases are ran-
domly shuffled. We then calculated the direct correspon-
dence between each of these surrogates and the four El Ni
˜
no
chronologies and one La Ni
˜
na chronology described above.
We find that the correspondence between the UEP and the
ENSO chronologies is statistically significant above the 99%
level. Further to this, the correspondence between the UEP
and these ENSO chronologies is larger than the correspon-
dence between the ENSO chronologies and any of the 10
original ENSO proxies.
In order to get a more accurate representation of the UEP
ENSO event capture we now make the assumption that the
effects of UEP defined ENSO events could be felt in the
ENSO chronologies in the year of the event or the year fol-
lowing (quasi-direct correspondence). This allows for the ef-
fects of annual averaging in the proxies and delayed ENSO
teleconnections in the chronologies. For example, consider a
typical El Ni
˜
no year, when taking an annual average of this
events SSTA it is normally attributed to the year the event
develops (year-0) instead of the year the event decays (year-
1). Now, if we consider the climatic effects of this event,
composite analysis reveals that El Ni
˜
no related South Amer-
ican rainfall anomalies also straddle year 0 and year 1 (Ro-
pelewski and Halpert, 1987). As such, a large rainfall event
that occurred and was documented in the year after the event
(year 1) could easily be attributed to the event in the year
prior. Using this quasi-direct approach we find that the num-
ber of corresponding El Ni
˜
no events between the UEP and
the chronology of Quinn and Neal (1992) changes from 51%
to 75.5%, while the number of corresponding La Ni
˜
na events
between the UEP and the chronology of Gergis and Fowler
(2009) changes from 57.3% to 85.5% (Table 5). We note
that while this jump in correspondence between the direct
to quasi-direct is quite large, it is consistent with expecta-
tions as this method essentially increases the degrees of free-
dom of the relationship. Using the same Monte Carlo type
approach as described in the paragraph above, we find that
the quasi-direct correspondence between the UEP and each
of the ENSO chronologies is again statistically significant
above the 99% level. Furthermore, this quasi-direct corre-
spondence between the UEP and these ENSO chronologies
is also larger than the quasi-direct correspondence of any of
the 10 original ENSO proxies.
5 ENSO variability since 1650
We have shown that the newly defined UEP provides an ac-
curate representation of ENSO variability, and while it cor-
responds well with the original proxy network, it provides
a better representation of observed indices and documented
historical chronologies of ENSO. Here we describe various
changes in the UEP, and hence ENSO, which have occurred
since 1650 focusing on (a) changes in ENSOs variance, (b)
multi-year ENSO events, (c) multi-decadal variability, and
(d) the effects of volcanic and solar forcing on ENSO.
5.1 Changes in ENSO variance
To assess the changes in ENSO variance since 1650 we
calculate the variance of the UEP in a 16-yr running win-
dow (Fig. 7b). However, prior to this the UEP variance
is scaled to that of the HadSST1 annual mean (July–June)
Ni
˜
no 3.4 region SSTA (Hn34) in the overlapping period us-
ing UEP×
σ
Kn34
σ
UEP
. The UEP is then extended to the year 2004
by adding the HadSST1 data onto the end of the UEP record
(Fig. 7a). Visual analysis of the 16-yr running variance re-
veals one obvious feature, a strong linear increasing trend.
Thus, the period from 1650 to the early 1700s a period of
low variance while the twentieth century is generally a pe-
riod of high variance. We note, however, that this increasing
trend is punctuated by a multi-decadal signal which creates
low and high variance periods relative to the variance trend.
There are various possible causes for these changes in ENSO
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10 S. McGregor et al.: The unified ENSO proxy
1650 1700 1750 1800 1850 1900 1950 2000
−1.5
−1
−0.5
0
0.5
1
1.5
Year
SSTA
a)
1650 1700 1750 1800 1850 1900 1950 2000
0
0.25
0.5
0.75
Year
SSTA Variance
b)
−2
0
2
4
PC1
Fig. 7. (a) The scaled UEP (gray) and its 13-yr low-pass filtered variability (black dashes); (b) The 16-yr running window variance of the
scaled UEP is shown in gray while the first PC of the running variance of all 10 input proxies is shown in black dashes.
variance. They are, that the variance changes represent: (i)
changed strength in the teleconnection patterns of ENSO; (ii)
dating uncertainties within thesource ENSO reconstructions,
which if present generally increase back through time, re-
ducing the amplitude of the common signal; (iii) proxy data
quality problems; or (iv) real ENSO variability. Here, we
will investigate each of these possible causes to try and as-
certain whether these changes in variance represent the real
ENSO signal.
Firstly, we note that there is high correspondence between
the running variance of the UEP and that of the annual av-
erage (July–June) of the verified Ni
˜
no 3.4 region SSTA of
Bunge and Clarke (2009), where the correlation coefficient
of 0.7 is statistically significant above the 99% level. As such
the 20th century variance of the UEP is realistic. Secondly,
while establishing the robustness of the UEP (see Methods
section) 252 first PCs were generated from the 10choose5
network. To assess the robustness of the of these variance
changes to the choice of input proxies used we calculated the
16-year running variance of each of these first PCs and com-
pared it (by calculating the correlation coefficient) with that
of the UEP. We find that all 252 correlation coefficients are
statistically significant above the 99% level and larger than
0.83. Thus, the changes in UEP running variance are robust
regardless of what proxies are selected from the original net-
work (i.e., whether proxies are included that were calibrated
to 20th century observations).
In regards to the effects of the changing strength of ENSO
teleconnection patterns on ENSO reconstruction variance,
we have effectively reduced the effects of this by identify-
ing the UEP in a network of ENSO reconstructions derived
from globally distributed proxy data. The use of a PCA on
the global proxy network diminishes the impact of regional
specific changes in the signal by identifying the dominant
changes in the entire network.
To identify the effects of proxy data quality problems we
first analyze the variability amongst the 10 different input
ENSO reconstructions. We would expect that if the quality
of the proxy reconstructions were to deteriorate an increase
in spread among the proxies would be seen. However, this is
not apparent (Fig. 1b), so we do not believe that the quality
of data systematically decays throughout the period 1650–
1977.
To understand the effects of dating uncertainties in the in-
put ENSO reconstructions we calculate the 16-yr running
variance of each of the original 10 proxies. Calculation of
the running variance reduces the effects of dating uncertain-
ties in the ENSO reconstructions by not allowing slightly
shifted signals to damp out the common signal variance. We
then seek to identify the common running variance signal
within these input reconstructions by calculating a PCA on
the running variances. The first PC of this analysis, which
accounts for 42% of the original proxy data variance, has
a correlation of 0.86 with the 16-yr running variance of the
UEP (Fig. 7b). Hence, the UEP running variance is repre-
sentative of the dominant variance changes in the original
ENSO proxies. We note however, that the time series of the
UEP running variance and the first PC of the original proxy
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S. McGregor et al.: The unified ENSO proxy 11
0
5
10
15
20
a)
0
5
10
15
20
25
b)
0
5
10
15
20
c)
0
5
10
15
ENSO count
d)
0
5
10
15
e)
0
5
10
15
20
f)
1550 1600 1650 1700 1750 1800 1850 1900 1950
0
5
10
15
20
g)
Year
Fig. 8. The number of El Ni
˜
no (red) and La Ni
˜
na (blue) events in a
30-yr centered running window from (a) the UEP (using a 0.5 stan-
dard deviation threshold for ENSO events), the historical chronolo-
gies of (b) Gergis and Fowler (2009), (c) Quinn and Neal (1992),
(d) Ortlieb (2000), (e) Garcia-Herrera et al. (2008), and the proxy
records of Bo
¨
es and Fagel (2008) and Hendy et al. (2003) which
again use a 0.5 standard deviation threshold for El Ni
˜
no events.
running variance do diverge near the beginning of the record
indicating that dating uncertainties begin to become impor-
tant.
This suggests that the changes in variance of the UEP rep-
resent a real signal in the original ENSO reconstructions. We
now assess these changes in UEP variance with historic doc-
umentary and unused paleo proxies to further verify the re-
alism of these variance changes. We find that both the in-
creasing trend in variance and the low variance period from
1650–1720 are supported by the 4 historical documentary
chronologies of El Ni
˜
no variability already discussed in this
study (Quinn and Neal, 1992; Ortlieb, 2000; Garcia-Herrera
1 2 3 4 5 6
0
10
20
30
40
Duration in years
Number of events
El Nino
La Nina
Fig. 9. The number of ENSO events that persist un-interrupted for
1-6-yrs.
et al., 2008; Gergis and Fowler, 2009). In regards to the in-
creasing trend in variance, there is a statistically significant
(above the 95% level) linear increasing trend in the number
of El Ni
˜
no events that occur in the 30-yr running windows in
the period starting 1650 and ending at the end of each record
of the four chronologies (Fig. 8b–e). While in regards to the
low variance period of the late 17th century each of these
chronologies displays a reduction in the number of El Ni
˜
no
events during this period (Fig. 8b–e). It is not likely that the
decreasing number El Ni
˜
no events back to this period or the
low levels in the late 1600s are due to lack of documentation
as all chronologies show a larger number of El Ni
˜
no events
in the period prior to 1650.
Further to this, both of these features are also supported by
the Great Barrier Reef coral luminescence record of Hendy
et al. (2003) (Fig. 8f). We note that while this time series
was used in the proxy network of Braganza et al. (2009) to
develop the ENSO proxy (ENSO proxy 9 in this study) for
the period 1727–1982, it was unused for the low variance pe-
riod (1650–1720) and thus provides additional independent
supporting evidence for the low ENSO variability of this pe-
riod. Additionally, the low variance period of 1650–1720
is also supported by the varved lake sediment proxy data of
Bo
¨
es and Fagel (2008) (Fig. 8g), while the linearly increas-
ing trend is supported by the Galapagos, El Junco lake di-
atom ratio’s (Conroy et al., 2009). Here, we note that while
the Bo
¨
es and Fagel (2008) data are from Southern Chile, at
a latitude that one would not expect to be significantly influ-
enced by ENSO variability, the data are significantly (>99%)
correlated (correlation coefficient of −0.58) with the SOI
between 1968 and 1997 and composites of rainfall variabil-
ity for June–August period following an El Ni
˜
no event dis-
play significant decreases in rainfall in the lake region (not
shown). As such, all the evidence available suggests that the
changes in UEP running variance are realistic and represen-
tative of changes in ENSO variance.
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12 S. McGregor et al.: The unified ENSO proxy
1875 1900 1925 1950 197
5
−
3
−
2
−
1
0
1
2
3
Year
Fig. 10. The normalized 13-yr low-pass filtered time series of the
UEP (gray) and PDO (black) along with time series of the IPO
(black dashes).
5.2 Multi-year ENSO events
In the early 1990s the tropical Pacific Ocean displayed
large scale El Ni
˜
no-like conditions which persisted for 5-yrs.
So unusual was this event in the observational record that
manuscripts discussing or seeking to understand this vari-
ability became prevalent in the scientific literature (Kessler
and McPhaden, 1995; Trenberth and Hoar, 1996; Latif et al.,
1997). The title of Latif et al. (1997) explains the range
of possible forcing mechanisms discussed in the scientific
literature, “Greenhouse warming, Decadal variability, or El
Ni
˜
no? An attempt to understand the anomalous 1990s”. In-
terestingly, given the observations of the 20th century the
study of Trenberth and Hoar (1996) estimates that the pro-
longed 1990–1995 El Ni
˜
no event should occur once in about
1500–3000 years. Here, we use the newly defined ENSO
proxy, the UEP, and a half standard deviation threshold to
classify ENSO events, to examine just how unusual this
multi-year behavior was in the context of ENSO variability
of the last 350-yrs. To this end, we calculate the number of
times in the UEP record (between 1650–1977) that El Ni
˜
no
(La Ni
˜
na) conditions persist for 1 through to 6 consecutive
years (Fig. 9). We find 7 (4) occurrences of El Ni
˜
no (La
Ni
˜
na) events that persisted for 3 years and 1 (3) occurrences
of El Ni
˜
no (La Ni
˜
na) events that persisted for 4 years. The
multi-year El Ni
˜
no events (persisting for 3 or more years)
occur reasonably frequently with approximately 2–3 events
occurring every 100-yrs, with only the 1600s not displaying
multi-year El Ni
˜
no events. It is a similar story for multi-year
La Ni
˜
na events with 2–3 events occurring every 100-yrs and
the 1600s not displaying any persistent negative events. Fur-
ther to this, we find one occurrence of both El Ni
˜
no and La
Ni
˜
na events that persist for 6 consecutive years in the 328
year record. Hence, while the prolonged El Ni
˜
no event of
the early 1990s was unusual, it is neither a one off, nor the
longest El Ni
˜
no event on record.
5.3 Multi-decadal variability
The shift towards more El Ni
˜
no-like conditions in the 1980s
and 90s and the two large magnitude El Ni
˜
nos of 1982/83
and 1997/98 generated a lot of interest in scientific com-
munity with many studies trying to ascertain the causes of
this unusual period. While anthropogenic climate change
was suggested as one of the possible mechanisms (
Trenberth
and Hurrell, 1994; Latif et al., 1997), the realization that this
change in the late 1970s was one of several large changes
that occurred during the 20th century (Trenberth and Hur-
rell, 1994; Zhang et al., 1997) led a large number of studies
to focus on how much of this apparent shift can be explained
by a naturally occurring multi-decadal signal. This Pacific
Ocean multi-decadal signal of the 20thcentury is represented
by either the Pacific Decadal Oscillation (PDO) of Mantua
et al. (1997), which is the North Pacific Ocean’s principal
mode of decadal variability, or the Interdecadal Pacific Os-
cillation (IPO) of Power et al. (1999), which is thought of
as the Pacific-wide representation of the PDO (Folland et al.,
2002).
More recent literature has questioned the statistical robust-
ness of the IPO/PDO as a climate mode, raising the pos-
sibility that the PDO/IPO could simply represent low fre-
quency variability of ENSO (Rodgers et al., 2004; Schopf
and Burgman, 2006). Regardless of its statistical robustness
as a climate mode, it is an intriguing question whether such
pronounced multi-decadal variability of the Pacific Ocean
existed during the 250-yrs prior to the 20th century. In order
to discuss the multi-decadal variability of the 20th century in
the context of the last 350-yrs, we firstly validate the multi-
decadal variability of the UEP (Fig. 7a) by comparing it with
the multi-decadal variability of observed indices of ENSO,
the IPO and PDO (Fig. 10) and the previously defined PDO
reconstructions of Biondi et al. (2001) and D’Arrigo et al.
(2001). Firstly, however, correlations between the multi-
decadal variability of the UEP and the original input proxies
are presented as a means to assess the contribution of each of
the original input proxies to the low frequency variability of
the UEP (Table 6). The relative magnitudes of these correla-
tion coefficients are very similar to the relative weightings of
the PCA (see Table 2), indicating that the low frequency vari-
ability of the UEP represents the multi-decadal variability of
the majortity of the original input recontructions.
Comparing the multi-decadal variability (defined as the
13-yr low-pass filtered data) of the UEP and that of the SOI,
Kn34, Hn34 and Bn34 gives correlation coefficients (R) of
−0.69, 0.66, 0.72 and 0.63 respectively and each of these
coefficients is statistically significant above the 95% confi-
dence level. While the correlation coefficient between the
multi-decadal variability of the UEP with that of the IPO
(which is 13-yr low-pass filtered by definition) for the period
1870–1980 is 0.69, whichis statistically significant above the
99% level. If we low-pass filter annual averages of the PDO
with the same 13-yr low pass filter and compare it with the
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S. McGregor et al.: The unified ENSO proxy 13
low-frequency variability of the UEP over the period 1900–
1980, we find correlation coefficient of 0.75 which is again
statistically significant above the 99% level. Comparing the
multi-decadal variability of the UEP with the multi-decadal
variability of two previously identified PDO proxies (Biondi
et al., 2001; D’Arrigo et al., 2001), we find statistically sig-
nificant (above the 95% level) correlation coefficients of 0.40
and 0.59 respectively. Thus, it appears that the UEP dis-
plays multi-decadal variabilitythatisconsistentwith the 20th
century variability of ENSO indices, the PDO and IPO, as
well as previously defined reconstructions of the PDO. As
such, the UEP also provides a means to reconstruct the multi-
decadal variability of the Pacific Ocean since 1650.
Focusing now on the PDO/IPO-like multi-decadal vari-
ability of the last 350-yrs we will assess whether this multi-
decadal variability has occurred throughout the last 350-yrs.
We will then also use the UEP and its low frequency com-
ponent to analyze whether phase of the PDO/IPO-type vari-
ability is related to the variance of ENSO as suggested by the
work of
Kirtman and Schopf (1998) and Federov and Phi-
lander (2000). Firstly, we find significant decadal variabil-
ity has persisted throughout the last 350-yrs (Fig. 7a) with
only a gradual reduction in the 60-yr running variance occur-
ring (figure not shown). As such, there are numerous periods
dominated by multi-decadal El-Ni
˜
no-like (La Ni
˜
na-like) be-
havior like the period from 1830–1860 (1750–1790). If we
now bin high frequency values of the UEP (calculated as the
residual when the low-frequency UEP is subtracted from the
UEP) according to the phase of the low frequency component
we do not find a significant relationship between the phase of
the PDO/IPO-type multi-decadal variability and variance of
the UEPs high frequency variability. This result is consistent
with the work of Yeh and Kirtman (2005).
5.4 The influence of Solar and Volcanic forcing
Solar variability and explosive volcanic events are both
mechanisms that can alter the natural radiative forcing of
the climate system. The previous studies of Clement et al.
(1996); Cane et al. (1997); Mann et al. (2005) refer to an
ocean dynamic thermostat mechanism whereby the tempera-
ture of the eastern equatorial Pacific varies negatively with
changes in radiative forcing. The study of Meehl et al.
(2009) discusses the joint effects of the top-down strato-
spheric ozone mechanism and the bottom-up coupled air-sea
mechanism which cools the equatorial Pacific in response to
peaks in solar forcing. Changes in radiative forcing have
also been proposed to modulate the variance of ENSO (Mann
et al., 2005), whereby low solar forcing periods are proposed
to have higher ENSO variance. Using the UEP along with
records of solar variability and volcanic forcing we will ex-
amine whether the associated radiative forcing plays any role
in the mean state or variability changes of ENSO in the last
350-yrs.
Table 6. Correlation coefficient between the 13-year lowpass fil-
tered (LPF) Unified ENSO proxy (UEP) and the 13-year lowpass
filtered original proxy network during the overlapping period. Sta-
tistical significance of greater than the 99% (90%) level in the cor-
relation coefficients is indicated by bold font (italic font).
Correlation LPF UEP
LPF Proxy 1 0.76
LPF Proxy 2 0.80
LPF Proxy 3 0.85
LPF Proxy 4 0.78
LPF Proxy 5 0.10
LPF Proxy 6 0.50
LPF Proxy 7 0.67
LPF Proxy 8 0.76
LPF Proxy 9 0.87
LPF Proxy 10 0.36
Here we investigate the statistical relationship between so-
lar induced changes in radiative forcing and the mean state
and variance of ENSO since the year 1650. We test the null
hypothesis: solar variability has no influence on the mean
state, median or variance of ENSO variability. To this end,
we use the reconstruction of solar irradiance of Lean et al.
(1995) which spans the period 1610–1994 (see Fig. 2a of
Lean et al. (1995)). High (low) solar forcing periods are de-
fined as those years in which the solar forcing is one half a
standard deviation or more above (below) the mean. We then
bin values of the UEP that correspond to high and low solar
variability years separately and test the null hypothesis de-
fined above. Using at-test we find that there is no statistically
significant shift in the mean ENSO state between high and
low solar variability phases. Further to this, using the Mann-
Whitney U-test we find no statistically significant change in
the ENSO median between the high and low solar variance
phases. Thus, this result does not support the proposed ther-
mostat mechanism of Clement et al. (1996). However, using
the F-test to assess whether there is a change in variance be-
tween the high and low solar phases we find that there is a
shift variance that is statistically significant. The result how-
ever does not support the results of Mann et al. (2005) as it
is found that as solar forcing increases, so to does the vari-
ance of ENSO. We note, that if the linear increased trend
in solar forcing is removed there is no longer a statistically
significant shift in ENSO variance. As such, this relationship
between solar forcing and ENSO variance is due to the strong
linear increasing trend in both solar forcing and ENSO vari-
ance, which does not support any causality. Hence, the null
hypothesis can not be rejected with a high degree of confi-
dence. We note that the statistical approach used to asses the
relationship between solar forcing and ENSO mean state and
variance is a good first step, however, it does not account for
non-linearities in the climate system response to changes in
solar forcing.
www.clim-past.net/6/1/2010/ Clim. Past, 6, 1–17, 2010
14 S. McGregor et al.: The unified ENSO proxy
−2 −1 0 1 2 3 4 5
−3
−2
−1
0
1
2
3
Normalised SSTA
Year after Eruption
a)
−2 −1 0 1 2 3 4 5
−3
−2
−1
0
1
2
3
Normalised SSTA
Year after Eruption
b)
Fig. 11. Composites of the UEP around years in which explosive volcanism occurred where volcanic events are defined using the (a) IVI
and (b) ICI indices. The solid black line indicates the composite mean while the gray dashed line indicates the composite median.
Consistent with the thermostat mechanism, past studies
have suggested that explosive volcanic events (which reduce
radiative forcing) tend to lead to a higher probability of El
Ni
˜
no type events in the years following the eruption (Adams
et al., 2003). Here we reassess these findings over the pe-
riod 1650–1977 using the UEP, the ice-core volcanic index
(IVI) of Gao et al. (2008) and the ice-core index (ICI) of vol-
canic events by Crowley et al. (2008). To this end, both the
IVI and ICI data were then discretized into a binary format
where 1’s (0’s) indicate whether a volcanic event occurred
(did not occur) in the given year. A subset of the UEP data
was then made which incorporated an eight year window of
data from the UEP (consisting of the 2-yrs prior to the event,
the event year and the 5-yrs following the event) for each of
the recorded volcanic events. Visual analysis of these data
subsets reveals two distinct features, (i) a shift in the data
towards more El Ni
˜
no like conditions in the year of the vol-
canic eruption, and (ii) a shift in the data towards more La
Ni
˜
na like conditions approximately 2–3 years after the vol-
canic eruption (Fig. 11). Both of these features are consis-
tent with Adams et al. (2003) and not inconsistent with the
results expected from the thermostat mechanism of Clement
et al. (1996).
Wenow specifically test thenullhypothesis: volcanic forc-
ing has no effect on the mean state, median or probability of
ENSO in the years during or surrounding the volcanic event.
We find that the distribution of the UEP shifts toward more El
Ni
˜
no-like state the year both the IVI or the ICI mark an erup-
tion event. Two statistical tests support the significance of the
shifts: the t-test (which shows that the change in mean state
is significant above the 95% level), and the Mann-Whitney
U-test (which shows that the change in the median is stati-
cally significant above the 95% level) (Fig. 11). However,
while the shift towards a La Ni
˜
na like mean state 2–3 years
after the volcanic event is apparent in both data subsets, the
shift in mean state and the median in either data subset is not
statistically significant.
Using a half standard deviation threshold to define dis-
crete ENSO events we find that probability of an El Ni
˜
no
event occurring in a given year goes from 26%, when vol-
canic events are not taken into account, to 58% in the year of
a volcanic event using the IVI defined events and to 70% in
the year of a volcanic event using the ICI defined events. As-
sessing the statistical significance of these changes in prob-
ability using a Monte-Carlo type approach, we find that the
IVI (ICI) defined shifts are significant above the 99% (95%)
level. The probability of a La Ni
˜
na event in any given year
goes from 25%, when volcanic events are not taken into ac-
count, to 55% (50%) 3-years after a volcanic event using the
IVI (ICI) defined events. Again, using the Mont-Carlo type
approach we find that this IVI (ICI) related shift in proba-
bility is significant above the 95% (80%) level. Thus, this
result supports the earlier work of Adams et al. (2003) and
suggests that changes in radiative forcing due to volcanic
Clim. Past, 6, 1–17, 2010 www.clim-past.net/6/1/2010/
S. McGregor et al.: The unified ENSO proxy 15
eruptions act to significantly alter the mean state and median
of ENSO while also significantly altering the probability of
ENSO events occurring. Hence, the null hypothesis can be
rejected with some degree of confidence.
6 Discussion and conclusions
In this manuscript we defined a new proxy of ENSO variabil-
ity covering the last 3 1/2 centuries, titled the “Unified ENSO
Proxy” (UEP). The UEP uses a PCA to combine the joint sig-
nal in the 10 selected, previously defined, reconstructions of
ENSO (Dunbar et al., 1994; Stahle et al., 1998; Cook, 2000;
Mann et al., 2000; Evans et al., 2001, 2002; Cobb et al., 2003;
Cook et al., 2008; Braganza et al., 2009). This results in a
robust ENSO index which displays an enhanced correspon-
dence with observations during the last century and with his-
torical chronologies of ENSO over the entire period. We note
here that each of the individual ENSO reconstructions used
as input for this manuscript could be affected by the changing
strength of ENSO teleconnections, dating uncertainties, bio-
logical biases, regional climatic factors and other non-ENSO
related large scale climatic modes. As such, so could the
UEP. However, each of the input reconstructions used that
was derived from multiple site proxies were processed by
the generating authors in order to extract the ENSO signal
away from this unrelated noise (Stahle et al., 1998; Cook,
2000; Mann et al., 2000; Evans et al., 2001, 2002; Braganza
et al., 2009). Additionally, here we carry out a PCA on these
pre-processed ENSO reconstructions, derived from a global
proxy network, which acts to further reduce the potential er-
rors due to site specific ENSO reconstruction biases.
Upon analysis of the UEP, the feature that stands out most
is the increase in variance through time. This feature is well
represented by the dominant mode of running mean variance
of the 10 original input proxies, meaning that it is a fea-
ture in the majority of the input proxies. Further to this, it
appears consistent with historic chronologies of ENSO and
three separate un-utilized proxies of ENSO variability. As to
the cause of this increase in variance, it is still an open ques-
tion. Interestingly though, this increase in ENSO variance
roughly coincides with an increase in Western Pacific warm
pool temperature (Newton et al., 2006). Consistent with the
ENSO variance changes displayed by the UEP, the increas-
ing temperature of the Western Pacificwarmpoolshouldlead
to enhanced ENSO variance, provided the subsurface ocean
remains at the same temperature (Sun, 2000). It was also
recently reported that the Inter-tropical Convergence Zone
(ITCZ) migrated northward between the year 1700 and 1850
(Sachs et al., 2009). The effect the northward migration of
the ITCZ would have on the variance of ENSO is unclear at
this stage. For instance, numerous studies conjecture that the
northward (southward) migration of the ITCZ should lead
to decreased (increased) ENSO variance (Haug et al., 2001;
Koutavas and Lynch-Stieglitz, 2004). However, model evi-
dence from the perpetual month experiments of Tziperman
et al. (1997) (where the Zebiak and Cane (1987) anomaly
models background annual cycle winds, ocean currents and
the associated ITCZ position are held fixed) is inconclu-
sive as it indicates that ENSO is extremely damped when
the ITCZ is both at its northern and southern most extremes
in September and February respectively. To further explore
the role of Western Pacific warm pool temperature changes
and the northward migration of the ITCZ on the variance of
ENSO, future work will investigate the associated changes in
zonal convection, flow divergence, thermal damping, Ekman
pumping and thermocline feedbacks by computing the sea-
sonally varying BJ index (Jin et al., 2006) of climate models
subject to modified WPWP temperature and ITCZ latitude.
Using the UEP we also analyzed several topical issues in
the ENSO literature, the occurrence of multi-year El Ni
˜
no
events, multi-decadal variability and the effects of solar and
volcanic forcing. In regards to multi-year or prolonged
ENSO events, a study by Trenberth and Hoar (1996) esti-
mated the prolonged El Ni
˜
no event of the early 1990s, which
persisted for 5-yrs by some measures, would only occur once
in approximately 1500–3000-yrs. Using the newly defined
ENSO proxy, the UEP, and a half standard deviation thresh-
old to classify ENSO events, we find that while the prolonged
El Ni
˜
no event of the early 1990s was unusual, it is neither a
one off and or the longest El Ni
˜
no event on record. Prolonged
ENSO events have occurred at least several times over the
last 3
1
2
centuries, with El Ni
˜
no events that persist for 3 or
more years generally occurring 2–3 times per century.
Looking at the multi-decadal variability of the UEP, we
find that (i) the UEP displays multi-decadal variability that
is consistent with the 20th century variability of the PDO
and IPO, (ii) significant decadal variability has persisted
throughout the last 350-yrs, and (iii) the phase of this multi-
decadal variability does not appear to influence the variance
of ENSO. Assessing whether changes solar forcing affect
ENSO mean state and variance of the last 350-yrs we find, no
conclusive support for a relationship on multi-decadal time
scales in contrast to the conclusions from the idealized mod-
eling study of Emile-Geay et al. (2007), and no conclusive
support for changes in the mean state (the thermostat mecha-
nism) in contrast to the study of Mann et al. (2005). However,
we find that volcanic forcing can induce a statistically signif-
icant change in the mean state and median of ENSO in the
years following the eruption. Further to this, we find that vol-
canic events can significantly alter the probability of El Ni
˜
no
(La Ni
˜
na) events occurring in the year of, and three years
after, the volcanic event, consistent with the earlier study of
Adams et al. (2003). A forthcoming study will further elu-
cidate how external volcanic forcing changes the timing and
characteristics of ENSO events.
www.clim-past.net/6/1/2010/ Clim. Past, 6, 1–17, 2010
16 S. McGregor et al.: The unified ENSO proxy
Acknowledgements. This work is supported by NOAA’s CCDD
program under grant number NA08OARH320910. The UEP
and its low-pass filtered decadal component is made available
on the World Data Center (WDC) for paleoclimatology and the
Asia-Pacific Data Research Center (APDRC).
Edited by: B. L. Otto-Bliesner
The publication of this article
was sponsored by PAGES.
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