Journal of the Meteorological Society of Japan, Vol. 80, No. 5, pp. 1199--1219, 2002

1199

Inter-hemispheric Decadal Variations in SST, Surface Wind,
Heat Flux and Cloud Cover over the Atlantic Ocean

Youichi TANIMOTO
Graduate School of Environmental Earth Science, Hokkaido University, Sapporo, Japan
Frontier Research System for Global Change, Yokohama, Japanand
Shang-Ping XIEInternational Pacific Research Center and Department of Meteorology, University of Hawaii, HI, USA(Manuscript received 9 October 2001, in revised form 27 June 2002)

Abstract
Atlantic decadal climate variations are studied using marine meteorological observations. To remove
artificial interhemispheric correlation, we perform empirical orthogonal function (EOF) analysis of sea
surface temperature (SST) variability separately for the North and South Atlantic. The first EOF for the
North (South) Atlantic in the decadal (8–16 years) band features a meridional tripole (dipole). In thetropics, the northern and southern leading EOFs form a meridional dipole with a center of action at
15 on either side of the equator. The leading sea level pressure (SLP) EOFs for the North and South
Atlantic each feature a center of action that is displaced poleward of the tropical SST extreme, at 30
latitude. The SLP center of action in the North Atlantic has a barotropic structure and contributes significantly to surface wind variability in the tropics. Despite being derived from statistically independent
data samples, the principle components for the leading SST and SLP EOFs (four in total) are significantly correlated with one another, indicative of the existence of an interhemispheric mode spanning theentire Atlantic Ocean. The same analysis for a longer SST record suggests that this pan-Atlantic decadal
variability exists throughout the 20th century.
In the North Atlantic, composite analysis of wind velocity and heat fluxes based on the PCs of theleading SST modes indicates that wind-induced latent heat flux is the major forcing for decadal SST
variability. In the South Atlantic, by contrast, wind anomalies are neither organized in space nor ingeostrophic balance with SLP, a problem likely due to poor sampling there as indicated by a comparison
with well-sampled satellite measurements.
Spatially coherent anomalies of low-level cloud cover are found to be associated with the tropicalAtlantic dipole, with increased (decreased) cloudiness over the cold (warm) lobe. These low-level cloud
anomalies do not appear to be associated with significant surface wind convergence, unlike the deep
convective clouds near the equator. By shielding solar radiation, these low-level cloud anomalies act toreinforce the underlying SST anomalies, reducing the Newtonian cooling rate for SST by as much as
30%.

1.
Corresponding author: Youichi Tanimoto, Graduate School of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan.
E-mail: tanimoto@ees.hokudai.ac.jp( 2002, Meteorological Society of Japan

Introduction

Tropical Atlantic sea surface temperatures(SSTs) affect the rainfall variability on surrounding continents including northeastern
Brazil, Sahel and western equatorial Africa

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Fig. 1. Scatter plots of zonal-mean (a) SST ( C) and (b) SLP (hPa) anomalies in the northern andsouthern tropics. Zonal mean anomalies are calculated from coast to coast using unfiltered borealwinter data in the 8–20 latitude for SST and 20–40 for SLP.

(Hastenrath and Heller 1977; Lamb 1978). Recent empirical and modeling studies further
suggest that boreal winter SST anomalies inthe tropical Atlantic may excite teleconnection
into the extratropics, affecting eastern North
America and Europe (Rajagopalan et al. 1998;
Watanabe and Kimoto 1999; Robertson et al.
2000; Okumura et al. 2001).
There is a general consensus that SST
anomalies (SSTAs) in the equatorial Atlantic
are involved in a zonal interaction with theatmosphere (Zebiak 1993; Huang et al. 1995;
Ruiz-Barradas et al. 2000), much akin to El
Niño/Southern Oscillation (ENSO) in the equatorial Pacific. Unlike the Pacific, however, SST
variance does not have a local maximum on theequator in the Atlantic; the variance is comparable or even larger in the subtropical (10–30 )than equatorial Atlantic. The cause of this subtropical SST variability in the Atlantic is notwell understood.
Previous attempts to empirically determine
the modes of tropical Atlantic SST variability
have yielded mixed and sometimes mutuallyconflicting results. Empirical orthogonal function (EOF) analysis suggests a monopole withthe same sign over the tropical Atlantic anda meridional dipole of opposing signs acrossthe ITCZ (e.g., Servain 1991), with the two explaining similar amounts of variance. JointEOF and/or Singular Value Decomposition(SVD) methods pick the dipole as the leading

mode, along with a well-known connection to
the meridional shift of the Atlantic ITCZ andanomalous precipitation in northeastern Brazil(Nobre and Shukla 1996; Chang et al. 1997). In
an attempt to relax the orthogonal requirementof the normal EOF analysis, Houghton andTourre (1992) rotate the EOFs to maximize regional variance and show that a pair of leading
rotated EOFs share similar amount variance,each with high loading confined to one side ofthe equator and little signal on the other side.
This, plus the fact that SSTAs are not linearlycorrelated across the equator (Fig. 1), leads
them to suggest that SST variability in thenorthern and southern tropical Atlantic is
physically independent, a conclusion supported
by Mehta’s (1998) analysis using a combination of statistical methods. When decadal timescales are separated from the interannual variability in the modal decomposition analyses,
the northern and southern tropical SSTAs in
boreal winter-spring are negatively correlatedon the decadal time scale, while those are positively correlated on the interannual time scales(Enfield et al. 1999, Tanimoto and Xie 1999).
Tropical Atlantic SST does not vary in isolation but rather in association with the extratropical Atlantic. The tropical North Atlantic
SSTAs are correlated with those in the extratropical North Atlantic, forming the so-called
wintertime tripole pattern (Deser and Blackmon 1993; Kushnir 1994; Halliwell and MayerOctober 2002

Y. TANIMOTO and S.-P. XIE

1996; Lau and Nath 2001). Similarly, SSTAs
in the tropical and extratropical South Atlantic are correlated (Venegas et al. 1997). On
decadal time scales, observational studies suggest that SST variability may be correlated
over the whole Atlantic basin from the South
Atlantic to Greenland (Xie and Tanimoto 1998;
Rajagopalan et al. 1998; Tourre et al. 1999;
Tanimoto and Xie 1999). During boreal winterspring seasons, as part of this so-called panAtlantic decadal oscillation (PADO), the tropical dipole, with a strong cross-equatorial SST
gradient that forces the ITCZ to move in themeridional direction, is apparently involved in a
two-way tropical-extratropical interaction with
the NAO (North Atlantic Oscillation) as modelstudies suggest (Xie and Tanimoto 1998;
Okumura et al. 2001; Sutton et al. 2001). Whena northward cross-equatorial SST gradientoccurs, sea level pressure (SLP) anomaly fieldshows a southward gradient, whose centers ofaction are displaced poleward of maximum
tropical SSTAs and found at 30 (Tanimoto and
Xie 1999). Some atmospheric general circulation models (GCMs) reproduce this SLP signal
as a wintertime response to a tropical SST
dipole (Okumura et al. 2001; Sutton et al. 2001;
N.-C. Lau, personal comm.), while some showno response in any season (Dommenget and
Latif 2000; Chang et al. 2000). The physicalmechanisms for tropical and extratropical interaction in the Atlantic remain to be further
studied.
The present study revisits the issue of interhemispheric structure of decadal variability
in the Atlantic by taking a different approachthan previous studies. Most previous studies
applied modal decomposition analyses to adomain that includes both the northern andsouthern tropical Atlantic, and then examined
spatial loadings. This may force an artificial
correlation across the equator as Houghton andTourre (1992) illustrated. To avoid this possibility of artificial interhemispheric correlation,
we carry out EOF analysis separately for the
North and South Atlantic. Recognizing tropicalextratropical interaction, we will include extratropical Atlantic in our analysis domains.
Thus, we do not assume a priori that there isan interhemispheric mode but instead check
posteriorly whether such an interhemisphericcorrelation exists by correlating the leading1201

principal components for the North and South
Atlantic and comparing their spatial patterns.
We will further repeat the same analysis for
the SLP field and examine if the leading EOFs
are correlated between the North and South
Atlantic and with the SST EOFs. This will giveinformation as to the interaction of the ocean
and atmosphere.
On interannual to decadal time scales, surface heat flux forcing by the atmosphere is
the major mechanism for SST variability in
off-equatorial oceans (Alexander 1992; Cayan
1992; Wagner 1996; Lau and Nath 1996, 2001;
Carton et al. 1996). In the tropics, an oceanatmospheric feedback mechanism involving
surface wind, evaporation and SST (WES; Xie
and Philander 1994) favors a mode of equatorially anti-symmetric structure that seems
to correspond to the SST dipole in the tropicalAtlantic (Chang et al. 1997; Xie et al. 1999).
Lagged crosscorrelation between meridional
wind anomalies and SST gradient near theequator presents a symmetric distribution ofcorrelation as a function of lead-lag months,
which indicates two-way air-sea interaction
at least in the equatorial region (Chang et al.
2001). In addition to wind-induced evaporation, the atmosphere can change SSTs throughcloud-induced radiation anomalies (Klein andHartmann 1993; Philander et al. 1996; Parkand Leovy 2000). Recently, Okumura et al.
(2001) report a positive feedback between SST
and low-level clouds in the tropical Atlantic inanalyzing observational and GCM data. In thispaper, we will examine the latent, sensible andradiative heat flux associated with the leading
SST EOFs in the Atlantic basin and investigate
whether these flux anomalies can explain the
SST anomalies fully or something else such as
ocean dynamics may also play a role.
Two types of gridded SST datasets are available. One type uses optimal interpolation to fill
data at every grid point in order to provide
boundary conditions for atmospheric GCMs.
The global sea ice and SST (GISST; Parker
et al. 1994) dataset belongs to this category.
The other gridding method fills a grid point
only when there are enough number of observations; the comprehensive ocean-atmospheredataset (COADS) is such an example. Many
empirical studies (Mehta 1998; Enfield et al.
1999; Xie et al. 1999) have employed the former

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Journal of the Meteorological Society of Japandataset to capture basin-scale structure of Atlantic SST anomalies. Over data sparse region
like the South Atlantic, however, heavy temporal and spatial interpolations can be a source of
signal distortion. Here we construct a dataset
using the ship-board measurements alone andrequire all grid points in the analysis to haveuninterrupted seasonal means (see the next
section for details) to ensure the fidelity of thegridded data and check the results using the
GISST data.
The rest of the paper is organized as follows;
Section 2 describes the datasets and the analysis procedures. Section 3 shows the leading
EOF modes that happen to explain the meridional gradients of the inter-tropical SST and
SLP anomaly fields. Section 4 presents the panAtlantic patterns for SST, SLP, wind velocity
and surface turbulence heat fluxes. Section 5
describes the associated cloud anomaly pattern
and its feedback on SST. Section 6 discusses
the possible mechanisms for the pan-Atlantic
variability and Section 7 is a summary.
2.

Data and analysis procedure

A monthly 4 latitude-longitude dataset ofmarine meteorological variables is constructedfrom quality-controlled ship and buoy observations compiled in Long Marine Reports in
fixed length records (LMRF) of comprehensive
ocean-atmosphere dataset (COADS; Woodruff
et al. 1987) for the North and South Atlantic (70 N–40 S) from 1950 through 1995. The
domain contains the Norwegian, North andCaribbean Seas, the southern part of the Labrador Sea, Gulf of Mexico and Mediterranean.
In the present study, we examine SST, SLP,
vector and scalar wind speed, sensible and latent heat flux fields. Higher resolution (2  2 )dataset of total and low-level cloudiness is also
constructed from ship reports in COADS LMRF
that are based on visual estimate of cloud coverage. Turbulence heat fluxes are calculatedusing Kondo’s (1975) aerodynamic bulk method
for each ship-buoy measurement and averaged
into the monthly dataset. The solar radiationand net longwave radiation are estimated by
the formula of Reed (1977) and Clark et al.
(1974), respectively. Details of calculation procedure are described in Tanimoto et al. (1997)
and Iwasaka and Hanawa (1990).
In constructing this dataset, we do not use

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satellite-based observations to avoid possible
systematic bias due to instrument changes. No
spatial interpolation is made so this new dataset serves as a strong check against optimallyinterpolated datasets like GISST. Because of
a possible systematic bias of SST data aroundthe 1940s due to changes in the number of observations and instrumental technique (Deserand Blackmon 1993; Folland and Parker 1995),
we will only use the COADS for a 46-year
period after 1950.
We will use the GISST (1900–1997) to infer
the modal structure in earlier decades that are
not well covered by COADS. We will further
employ historical surface temperature dataset based on only instrumental observations(Parker et al. 1995; Jones et al. 2001) to validate the results from GISST. In regions likethe South Atlantic where there are few shipobservations, COADS suffers severe sampling
errors; SLP and wind velocity may not evensatisfy the geostrophic balance. To complement results based on the COADS, we use themonthly 2 latitude-longitude National Centers
for Environmental Prediction (NCEP) reanalysis dataset (Kalnay et al. 1996) for 1958–
1995 that provides dynamically consistent SLP
and wind velocity data. At each grid point, we
calculate the monthly climatological mean annual cycle based on the entire period of record(COADS: 46 years; NCEP: 38 years; GISST:
98 years), and the monthly anomalies are defined as the departures from the climatologicalmeans.
The NAO and associated SST tripole pattern
are very pronounced in boreal winter but is notseen in summer (Deser and Blackmon 1993;
Kushnir 1994). To ensure characteristics of the
PADO, we further construct averages for boreal
winters (December through March) and require
more than three monthly-mean values each
winter to fill a grid point. Our EOF analysis of
COADS SST and SLP anomalies is carried out
only at those grid points that contain no missing boreal-winter averages for the entire periodof 1950/51 through 1994/95. There are 312(103) such grid points for the North (South)
Atlantic SST field, and 314 (113) for the North(South) Atlantic SLP. Uninterrupted data
record for 46 years may be too strong a requirement more than enough to ensure ameaningful EOF analysis, but we intentionallyOctober 2002

Y. TANIMOTO and S.-P. XIE

set this rather stringent criterion for data inclusion to contrast with the GISST analysis.
Large variance is found in the tropical SST
anomaly field on interannual and decadal timescales (Servain 1991; Huang and Shukla 1997;
Mehta 1998) with a spectral gap between the
two time scales (Mehta and Delworth 1995;
Mehta 1998; Enfield et al. 1999). A closer lookof the spectral structure for pan-Atlantic SST
variability by Tourre et al. (1999) also presentsinterannual (2.7-, 3.5-, and 4.4-year) and quasidecadal (11.4-year) time scales of which spatial
structures represent different coherent patterns to each other. Significant spectral peaks
above the red noise are also identified at decadal time scales in other variables such as
northwest Brazil rainfall (Mehta 1998), and835-year high-resolution sediment core in thesouthern Caribbean Sea (Black et al. 1999).
Based on the above spectral structure of tropical Atlantic SST anomalies, Tanimoto and Xie(1999) applied a time-scale separation method
to the 51-year SST time series and found negative (positive) correlation on decadal (interannual) time scale in SST anomalies across theequator. Here we use a four-pole Butterworth
recursive band-pass filter similar to Tanimotoand Xie’s to extract decadal variations. Cut-off
periods of the band-pass filter are eight andsixteen years, respectively. The decadal SST(SLP) variance represents 14% (11%) of the un-1203

filtered winter variance over the whole Atlanticbasin.
3.

Inter-hemispheric structure

Before performing the EOF analysis, we divide the Atlantic basin into two parts north andsouth of the equator to eliminate possible artificial correlation between them that may arise
from EOF decomposition as discussed in Section 1. Our analysis domains include both thetropical and extratropical Atlantic: 0–70 N and
0–40 S (0–50 S) for COADS (GISST). Then,SST anomalies are averaged for boreal winterand filtered through a decadal band of 8–16
year, a frequency domain that contains large
SST variance (Servain 1991; Mehta 1998; Rajagopalan et al. 1998; Tourre et al. 1999).
3.1 COADS (1950–95)
The upper two curves in Fig. 2 show normalized principle components (PCs) of the first
SST modes in the North (solid) and South(dashed) Atlantic, respectively, based on
COADS. Figure 3 depicts the SST regressions
onto these PCs. Grid points used in the EOF
analysis are shaded in the background. The
loadings of these leading EOFs explain 35.0%
and 28.9% (4.8% and 4.0%) of the band-passed(unfiltered) variance at all the grid points overthe entire Atlantic basin, both north and south(Table 1). The percentages to the total decadal

Fig. 2. Upper curves: normalized principal components (PCs) of SST anomalies in the North (solid)
and South (dashed line) Atlantic. Lower: PCs of SLP anomalies.

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Fig. 3. The first SST EOFs for the North and the South Atlantic, which explain 35.0% and 28.9%(4.8% and 4.0%) of decadal band-passed (unfiltered) boreal-winter SST anomalies over their respective domains. Grid points used in the EOF analysis are shaded. SST regression onto the North(South) Atlantic PC is shown in the left (right) panel. Contour interval is 0.1 C, with negativevalues dashed.

Table 1. Explained percentage of leading
EOFs to the total unfiltered (left column) and decadal (right column)
variances of particular domains. Upper(lower) two matrices are for a domain ofNorth (South) Atlantic.

and unfiltered variances of a particular subdomain are also listed in Table 1. The firstmodes accounts for 37.0% and 54.9% of thedecadal variance in the North and South Atlantic, respectively, and are well separated
from the second modes, which explain only
19.0% and 13.2% of the decadal variance,
respectively. The leading EOF for the North
Atlantic features the tripole mode (Deser andBlackmon 1993; Kushnir 1994) with positive
centers of action in the tropics and high latitudes (55 N) and a negative center in between

in the midlatitudes (40 N). The South Atlantic
EOF shows a rather uniform negative loading
except in the southeastern corner where thepoor data coverage gives rise to a spurious
center of action. The spatial structure of SST
anomalies will be further discussed in Section
4.
We note that these leading EOFs are derived
from statistically independent subsets of the
COADS so there is no a priori reason for them
to be correlated. Yet the PCs of these separate
EOFs are positively correlated (Fig. 2), with asimultaneous correlation coefficient of 0.63. In
the tropics, their centers of action have opposing signs across the equator (Fig. 3). The regression patterns in Fig. 3 are very similar to
the PADO of Xie and Tanimoto (1998). Simultaneous correlation maps with the two PCs
show similar spatial pattern to the regression
maps (not shown), with the correlation coefficients exceeding 0.8 at all the centers of action.
We repeat the same analysis for the borealwinter SLP fields in the two sub-domains (Fig.
4). The loadings in leading SLP modes explain
35.9% and 18.0% (4.0% and 2.0%) of bandpassed (unfiltered) variance in the Pan-Atlanticbasin. A negative SLP center of action appears
in the 20–40 N band in the subtropical North

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1205

Fig. 4. Same as Fig. 3, but for SLP. These EOFs explain 35.9% and 18.0% (4.0% and 2.0%) of decadal
band-passed (unfiltered) boreal winter SLP anomalies for the North and the South Atlantic, respectively. Contour interval is 0.2 hPa.

Table 2. Correlation between the leading
PCs of SST and SLP variability in the
North and South Atlantic.

Atlantic, which is 10–15 poleward of the positive SST center in the tropics. The extratropicalcenter west of Europe also has significant regressions. The South Atlantic loadings do nothave much spatial structure, but with a slightsouthward gradient SLP. The accompanying
PCs of the SLP EOFs for the North (solid) andSouth (dashed line of Fig. 2) Atlantic correlate
to one another. Furthermore, the SST and SLPpairs of PCs are correlated among themselves
despite that they are derived from independent
samples (Table 2). In particular, two minima
in the early 1970s and 1980s and three maxima
in the late 1960s, late 1970s and early 1990s
appear in all four time series (Fig. 2). These
results indicate that a PADO involving SST
and SLP is quite pronounced for the recent
three decades. Scatter plots of zonal meanvalues in the 20–40 N and 20–40 S bands, cal-culated from band-pass filtered SLP anomalies(not shown), confirm an out-of-phase relationship with a tilted elliptical track much like thescatters of decadal SST anomalies (see Fig. 5 in
Tanimoto and Xie 1999).
We also perform an SVD analysis for the SST
and SLP fields over the combined North andSouth Atlantic. The leading SVD mode explains
60.1% of total squared covariance. The heterogeneous regression maps of SST and SLP fields(not shown) are similar to the regression fields
in Figs. 3 and 4. This is consistent with Tourre
et al. (1999). This PADO pattern with zonal
bands of SSTAs with alternating signs is simulated in ocean models forced by observed windvariability (Seager et al. 2000) and in a coupled
GCM (Watabane et al. 1999).
3.2 GISST (1901–97)
The correlation among four PCs in Fig. 2
is highest for the three decades from 1966
through 1990. (If performed for a shorted
period of 1966–1995, the EOFs for SST and
SLP explain about 15% more decadal variancein each sub-domain.) Before 1966 and after
1990, however, their correlation is not as good.
Since this could be due to the end effect of the
band-pass filter, we use the longer GISST dataset and perform the same EOF analysis forthree periods: the entire 20th century (1901–

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Table 3. Explained percentage of leading
EOFs to the total unfiltered (left column) and decadal (right column)
variances of particular domains. Upper(lower) three matrices are for a domain
of North (South) Atlantic. The left
(middle, right) matrices represent percentages during the period of 1901–97(1950–97, 1901–49).

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1997) and its first (1901–1949) and second(1950–1997) halves. The percentages to thetotal decadal and unfiltered variances of a particular sub-domain over each period are also
listed in Table 3. It is not surprising thatthe leading EOFs for 1950–1997 (not shown) is
nearly the same as those in Fig. 3. Most updated PCs for the entire Atlantic are available
at the author’s web site (see appendix).
The North Atlantic EOF for 97 years of the20th century (Fig. 5a) is also similar to that for
the recent four decades except for a more zonally uniform structure in the tropics. The South
Atlantic EOF for 1901–1997 appears quite different from that for 1950–1997 with a dipole

Fig. 5. Same as Fig. 3, but for GISST during (a) 1901–97 and (b) 1901–1949. Contour interval is0.05 C, with negative values dashed. Table 3 displays the variance explained by these EOFs. The
upper (lower) curve in the bottom panel shows the North (South) Atlantic PC.

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Table 4. Correlation between the leading PCs of SST variability in the Northand South Atlantic for three different
periods.

structure between the tropics and midlatitudes
as opposed to a uniform field in Fig. 3. A close
look suggests that these differences arise
rather from the lack of midlatitude South Atlantic information in the COADS analysis due
to our stringent requirement of having undisrupted coverage at every grid point. This South
Atlantic dipole has been discussed by Venegas
et al. (1997). The North and South Atlantic PCs
are again positively correlated (Table 4).
The right panels of Fig. 5 are the EOFs
for the first half of the 20th century, which resemble those to the left with some differencesin details. We note that the North Atlantic tripole now becomes the second mode explaining
28.2% of the total variance. The first North
Atlantic mode for this period explains slightly
more variance (29.7%, not shown) but highloadings are confined only along the east coastof North America. Hence trivial degeneracy andmixing of eigenvalues may not occur among
these two modes. The North Atlantic tripole isstill highly correlated with the South Atlantic
dipole for this earlier period (corr ¼ 0.57; Table4). The correlation between the North andSouth Atlantic PCs improves significantly for
1950–66. Thus the apparent variable correlation in our COADS analysis is indeed due tothe end effect of our band-pass filter.
One may cast doubt on the usefulness of
our EOF analysis for the earlier period on the
grounds that the optimal interpolation scheme
for GISST uses the EOFs for the later period
to fill data voids. To address this concern, we
carry out a composite analysis by choosing
twelve (eleven) years when the PCs for 1901–
1949 are in the positive (1901–02, 1909–10,
1926–27, 1940–45) and negative (1912–13,
1921–24, 1935–36, 1947–49) phase. (TheFig. 6. Difference of unfiltered borealwinter surface temperature between
the positive and negative phases of thePCs for 1901–49. Contour intervals are
0.2 C, with negative values dashed.

World War I period has not been included.)
Figure 6 shows the difference between thosepositive and negative phase years, based on the
instrumental observations both on the land andover the ocean (Parker et al. 1995; Jones et al.
2001). The difference map is quite similar to
the EOFs derived from the heavily spatialinterpolated GISST. Land temperature anomalies over northern South America, eastern
North America and Europe are quite well organized and have the same sign as those in theneighboring seas.
4.

Pan-Atlantic decadal variability

The above analyses of different datasets
using the EOF and composite techniques suggest that Atlantic decadal variability has a highdegree of interhemispheric coherence at decadal timescales and that this PADO mode isquite robust during most of the 20th century. In
this section, we focus on the three recent cyclesof the PADO, taking advantage of more abundant observations. Six years each are chosen to1208

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Fig. 7. Difference of unfiltered boreal-winter SST and SLP between six positive (1968, 69, 70, 79, 80,81) and six negative (1972, 73, 74, 84, 85, 86) PADO-phase years. Contour interval is 0.2 C and
0.5 hPa for SST and SLP fields, respectively. Negative values are dashed.

represent PADO’s positive (1968–1970, 1979–1981) and negative (1972–1974, 1984–1986)
phases. The positive phase here refers to anorthward SST gradient in the tropics. We willshow difference maps between the two phases
based on the COADS without any spatial
interpolation/smoothing.
4.1 North Atlantic
The difference map for SST (Fig. 7) in the
Northern Hemisphere is similar to the EOFs in
Figs. 3 and 5. The amplitudes are comparable
between the northern and southern tropics,
with maximum values at about 1 C. The positive pole in the midlatitude South Atlantic appears in this composite map much like that in
the EOF based on GISST. The SLP pattern(Fig. 7) in the North Atlantic is similar to the
EOF in Fig. 4 except for a reduced signal westof Europe. Though noisy over the South Atlantic, the SLP difference map indicates a consistent southward gradient up to 20 S in thetropics.
Anomalous wind velocity in the North Atlantic is well organized in space and apparently ingeostrophic balance with SLP anomalies (Fig.
8a). The anomalous easterlies north of 35 N
reduce the local wind speed in the mid- andhigh latitudes. South of the center of anomalously low SLP, the anomalous westerlies
reduce (increase) the speed of the prevailingtrade (westerly) winds south (north) of 30 N.
The maximum reduction in trade wind speed
reaches 1.5 ms1 (light shade in Fig. 8), suppressing local latent heat flux release from theocean surface (Fig. 9). The anomalous circulation at the surface induces large anomalies inlatent heat flux that act to cause local SST
anomalies (Alexander 1992; Cayan 1992; Lau
and Nath 2001). In the high-latitude North
Atlantic, the anomalous sensible heat flux (Fig.
9) is comparable with the latent one. A recent
atmospheric GCM study shows that the anomalous surface circulation in the tropical andmidlatitude North Atlantic, including the reduced trade winds and enhanced cold surge on
the east coast of North America, is a responseto tropical SST-induced changes in deep convection (Okumura et al. 2001).
The cross-equatorial southerlies are a robustfeature near the equator, in response to anorthward SST gradient anomaly. Anomalous
winds converge (diverge) just north (south) of
the equator, causing an anomalous northward
shift of the ITCZ (Fig. 10b) and droughts innortheastern Brazil.
4.2 South Atlantic
South of 10 S, anomalous wind vectors are
not well organized, and neither are the latentand sensible heat flux anomalies. The South
Atlantic (10–40 S) is very poorly observed; theOctober 2002

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1209

Fig. 8. (a) Same as Fig. 7, but for wind velocity anomalies (vectors with the scale in 5 ms1 displayed
at the bottom). Dark (light) shades denote scalar wind speed anomalies that exceed 0.5(0.5) ms1 . (b) Same as Fig. 8a, but for SLP and wind velocity from the NCEP reanalysis. Contour interval is 0.5 hPa for the SLP field, with negative values dashed.

Fig. 9. Same as Fig. 7 but for latent and sensible heat flux anomalies (upward positive). Contour
interval is 10 Wm2 . Same shades in Fig. 8a are superimposed.

total number of simultaneous observations of
SST, air temperature, relative humidity andwind velocity registered in COADS is 5.1  10 5
as opposed to 2.4  10 6 for the North Atlantic(10–40 N) for the period of 1961–1990. Overmuch of the South Atlantic, anomalous winds
are not in geostrophic balance with the SLP
even in a qualitative sense. Previous modal de-1210

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Fig. 10. (a) Difference of unfiltered February–April total cloud cover anomalies (contours at intervals of 3.0%) between positive and negative phases of the PADO, based on a 2  2 COADS.
(b) Zonal mean anomalies of SST (upper right) and cloud cover (%; solid) along with surface winddivergence (106 s1 in dotted line; lower right panel).

composition analyses (Enfield and Mayer 1997;
Mestas-Nuñez and Enfield 1999) also yield astrong SLP signal but no associated wind signal in the South Atlantic.
We repeat the composite analysis using the
NCEP reanalysis. In the North Atlantic, both
the resultant wind velocity and SLP maps (Fig.
8b) closely resemble those based on COADS.
In the South Atlantic, by contrast, anomalous
winds become much better organized, in geostrophic wind balance (down the SLP gradient)off (near) the equator. A positive SLP center
is found around 30 S, slightly poleward of thenegative SST center in the tropical South Atlantic. The southeasterly trades intensify northof this SLP center, implying an evaporative
cooling that is consistent with the observed
SST anomalies underneath. South of this SLP
center along 40 S, the NCEP reanalysis showsenhanced westerlies that would act to cool the
SST instead of a warming in the COADS composite. This inconsistency between observed
SSTAs and local winds in the extratropicalSouth Atlantic can be due to the poor sampling/reanalysis errors or an indication that ocean
processes other than surface heat flux andEkman advection may be at work there (Häkkinen and Mo 2002). We note that the Brazil
current leaves the coast and joins the Antarctic
Circumpolar Current around 40 S, along which
the southern lobe of the South Atlantic dipole islocated. Ocean dynamics is important for SST
variability in such western boundary current
extension regions (e.g., Tomita et al. 2002).
Among the three variables we analyze here,
SST varies on the slowest time scales (>1month) and is least subject to sampling errors.
SLP has large spatial scales (>1000 km) and isless susceptible to turbulence fluctuations thanwind velocity, which is related to the spatial
derivative of SLP. It is quite clear that traditional ship-board wind measurements are notenough to sample the South Atlantic properly.
The NCEP dynamic assimilation system significantly improves the wind product by including SLP measurements. Still, the inconsistency

October 2002

Y. TANIMOTO and S.-P. XIE

Fig. 11. (a) Zonal-mean standard deviations of zonal wind velocity (thick linewith closed circles) and SST (light linewith open circles) in the decadal band.
(b) Same as Fig. 11a, but for the unfiltered total variance for eight years
from COADS (line with filled circles)
and ERS satellites (dashed). The climatological mean is computed from theeight-year data used.

in the input data between wind velocity and
SLP is likely to have introduced errors in thewind analysis and obscured the wind signals on
climatic time scales.
Satellite scatterometers offer a great potential of improving wind measurements over traditionally poorly observed oceans such as theSouth Atlantic (e.g., Hashizume et al. 2001).
Figure 11 compares standard deviations ofboreal-winter zonal wind velocity observed

1211

by European Remote Sensing (ERS) satellites
for 1992–2000 and that based on COADS for
1987–1995. ERS scatterometers measure windvelocity averaged over a 0:5  0:5 grid box
and at a weekly resolution. Even over wellship-sampled North Atlantic, COADS windvariance tends to be larger than the ERS. Muchof the variance difference in the North Atlantic
may be due to difference in sampling. COADS
samples wind velocity at a single point and thus
contains high-frequency, small-scale fluctuations while ERS scatterometers measure largescale wind velocity averaged over a 0:5  0:5area.
To demonstrate the effectiveness of spacewind observations, Fig. 12 compares 1994–
1993 wind velocity differences based on COADS
and ERS, along with COADS SLP differences.
In the North Atlantic, COADS samples thewind field quite well, which is apparently ingeostrophic balance with concurrently measured SLP anomalies. The 1994–93 difference
fields feature a strong anomalous low (high)in the high- (mid-) latitude North Atlantic. In
particular, the mid-latitude high has two centers at 70 W and 20 W, respectively, which areboth associated with enclosed anticyclonic flow.
Thus, in the North Atlantic, the ERS wind
anomalies are similar to the well-sampled
COADS fields, both qualitatively and quantitatively.
By design, the ERS scatterometers sample
the South Atlantic winds at the same frequency
as in the North Atlantic. The 1994–93 winddifferences are weakly easterly (<3 m/s) in thetropical South Atlantic, with a maximum at0 E, 12 S, and feature weakened westerlies
in 30–40 S. The COADS wind differences are
generally noisy and not well organized in space(the southerlies off northeastern Brazil are theexception). The fact that the ERS wind anomalies are not in geostrophic balance with COADSSLP anomalies in the South Atlantic suggeststhat the latter also suffers from severe sampling errors, at least on interannual timescales.
5.

Cloud feedbacks

In boreal winter-spring seasons when the
SST field in the equatorial Atlantic is nearly
uniform in both the zonal and meridional directions, the ITCZ is sensitive to the changes
in interhemispheric SST gradient (Nobre and1212

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Vol. 80, No. 5

Fig. 12. (a) February–March difference of COADS SLP and wind velocity between 1994 and 1993.
Contour interval is 1 hPa. The wind velocity scale (ms1 ) is placed at the bottom. (b) Same as
Fig. 12a, but for wind velocity difference based on ERS scatterometers.

Shukla 1996). The associated meridional shiftsof the ITCZ cause northeastern Brazil rainfall
to fluctuate, leading to droughts or floods (Servain 1991; Mehta 1998; Chiang et al. 2000).
Figure 10a shows a difference map of cloudiness anomalies between positive and negative
phases based on a 2 resolution COADS. Associated with an anomalous northward gradientof zonal mean SST anomalies is an increase(decrease) in cloudiness at 5 N (5 S; Fig. 10b),indicating a northward shift in ITCZ’s latitude.
These near-equatorial changes in cloudinessseem associated with enhanced (suppressed)
convective activity over positive (negative) SST
anomalies as indicated by the convergence(divergence) of anomalous winds at the surface(dotted line in Fig. 10b). Thus, in the nearequatorial region, more high clouds form overwarm SST anomalies, reducing incoming solar
radiation and underlying SST anomalies. This
near-equatorial damping may not have mucheffect on the meridional SST gradient over thewhole tropics because SST anomaly extremes
are located near 15–20 N/S.
Additional cloudiness anomalies are foundover SST anomaly extremes poleward of 10
latitude on both sides of the equator. These offequatorial cloudiness anomalies, in contrast,

are negatively correlated with local SST anomaly, but apparently not associated with significant changes in surface wind convergence.
More than 90% of the anomalies in these subtropical regions are due to changes in low-levelclouds (Fig. 13a) such as stratus and stratocumulus. Over a positive SST anomaly, theboundary layer warming may reduce the static
stability at its top, a condition disfavoring thestratus formation (Klein and Hartmann 1993).
Even without any spatial smoothing in ourdataset, these low-level cloud anomalies arequite well organized in latitudinal bands between 10 and 20 (Fig. 10). The total radiativeflux is about 10 Wm2 cooling (heating) overpositive (negative) cloudiness anomalies, withshortwave dominating longwave forcing (Fig.
13b). The radiative forcing by low-level clouds
is a positive feedback on SST1. Such opposite
response of the equatorial and subtropical1

After the submission of this paper, an independent but complementary study came to our attention. By cross-correlating observed SST andradiative heat flux anomalies, Frankignoul and
Kestenare (2002) show that SST-cloud radiative
feedback is positive in most of the South Atlantic
and negative near the equator, a result in support of ours.

October 2002

Y. TANIMOTO and S.-P. XIE

1213

Fig. 13. (a) Ratio of low-level to total cloud cover anomalies in Fig. 10a. The regions with this ratio
exceeding 90% are shaded. (b) The Feb.–Apr. downward total radiative flux anomalies at the sea
surface (contours). Contour interval is 5 Wm2 . The heavy (light) shading denotes shortwave
heating (cooling) rate over 5 Wm2 .

clouds in the Atlantic is captured to someextent in recent stand-alone (Okumura et al.
2001) and hybrid-coupled (Xie and Saito 2001)
atmospheric GCM simulations.
This SST-stratus feedback is important forkeeping the climatological ITCZ north of theequator in the Pacific (Philander et al. 1996).
Here we show that it also operates on decadal
time scales in the off-equatorial tropical andsubtropical Atlantic. In a coupled oceanatmosphere model, the temporal spectrum of
the dipole mode response to white-noise forcing
is sensitive to the damping rate in the SST
equation (Xie and Tanimoto 1998; Xie 1999).
The SST dependence of surface evaporation is amajor mechanism for thermal damping, which
can be linearized as a Newtonian cooling termwith an e-folding time of 1 year (Xie 1999). The
rate of SST change due to radiative forcing
by low-level clouds is 0:62ð1  AÞS0 C 0 /(cp rH),
where we have used Reed’s (1977) formulafor shortwave radiation, S0 ¼ 300 Wm2 is thesolar radiation in clear sky, A ¼ 0.96 thealbebo of sea surface, C 0 the perturbation cloudamount, cp and r are the specific heat at constant pressure and density of sea water, and
H ¼ 50 m is the mixed layer depth. Assuming that low-level clouds in off-equatorial tropics vary with local SSTs, we have C 0 ¼ aT 0
with a ¼ 0.1 K1 from the right panels of
Fig. 10. This leads to an SST-stratus feedback coefficient, b ¼ 0:62að1  AÞS0 /(cp rH) ¼(3.5 years)1 . Thus the local SST-stratus feedback can reduce the Newtonian cooling rate for
SST by as much as 30%.
Decadal SST variance in boreal winter features a local maximum on either side of theequator (Fig. 11a). Yet zonal wind variance ismuch smaller in the southern than northern
subtropics. This latitudinal asymmetry of zonal
wind variance is also present on interannual
time scales in both the ship-board and satellite
datasets (Fig. 11b). By contrast, the cloud
anomaly pattern in the South Atlantic is robust
and present in all seasons (Fig. 14). On boththe annual and boreal-winter means, the cloudiness anomalies in the southern subtropics areabout twice as large as the northern ones. Such

1214

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Vol. 80, No. 5

Fig. 14. (a) Annual-mean PADO composite for total cloud cover (contour at intervals of 3.0%, withlight shade <3.0% and heavy shade >3.0%). (b) Normalized interhemispheric differences between
the northern and southern tropics of cloud cover (solid circles) and SST (open circles). Here, unfiltered annual-mean anomalies are averaged zonally from coast to coast and meridionally in the
15–25 (10–20 ) band for cloud (SST). (c) Decadal cloudiness anomalies averaged in 15–25 over
the South (solid) and North (open circles) Atlantic, as a function of calendar month. The sign of theNorth Atlantic anomalies is reversed.

stronger stratus feedback can help the tropicalSouth Atlantic maintain a level of SST variability comparable to the North Atlantic. We
caution, however, that the true level of windvariability in the South Atlantic remains to be
determined from longer satellite observations
as has been discussed in Section 4.2.
6.

Discussion: Origin of PADO

The poleward displacement of the SLP centerfrom the tropical SST center in the North
Atlantic—and to a lesser degree, in the South
Atlantic—is quite peculiar and inexplicablewith baroclinic models linearized about a resting state; such models predict SLP and SSTA
centers more or less coinciding with each other(Matsuno 1966; Gill 1980; Lindzen and Nigam
1987). Such discrepancies between linear baroclinic models and observed surface wind vari-ability in the tropical Atlantic are independently noted by Okumura et al. (2001), Seager
et al. (2001) and Chung et al. (2002). A comparison of the composite height anomalies at1000 and 500 hPa (Fig. 15) explains this deficiency of baroclinic models; Most of the SLP
anomalies are the surface signature of a barotropic stationary seesaw between the subtropical and high-latitude North Atlantic. Because
the barotropic height anomaly center at 35 N
induces surface wind anomalies that extend
well into the tropics, atmospheric models forthe tropical Atlantic will need to include thisbarotropic mode and its interaction with baroclinic modes and the mean flow.
From observations alone, it is difficult todetermine the origin of the NAO-like barotropic seesaw in the extratropical North Atlantic. Based on a single realization of atmosphericOctober 2002

Y. TANIMOTO and S.-P. XIE

1215

Fig. 15. Same as Fig. 7, but for geopotential height at 1000 hPa (thin contours) and 500 hPa (thick)from the NCEP reanalysis.

GCM simulation forced by observed SST history, Robertson et al. (2000) and Watanabe andKimoto (1999) suggest that the shift in the Atlantic ITCZ may induce a barotropic responsewith a strong projection on the NAO. Okumura
et al. (2001) force an atmospheric GCM witha time-invariant SST dipole in the tropicalAtlantic and make a long-time integration to
suppress the internal chaotic variability of theextratropical atmosphere. They obtain a robust
barotropic response in the subtropical North
Atlantic much like the one in Fig. 15 withanomalously intense cold surges on the east
coast of North America. Similar results are obtained by using the ensemble simulation technique (N.-C. Lau 2000 personal comm.; Sutton
et al. 2001). Coupling their atmospheric GCM
with a slab ocean mixed layer, Okumura et al.
further show that the extratropical part of theNorth Atlantic SST tripole is partly forced by
the tropical SST dipole through this barotropic
atmospheric bridge.
These GCM results may imply a tropics-toextratropics forcing, but extratropical SSTAs

may also induce NAO-like response as suggested by Rodwell et al. (1999) and Mehta et al.
(2000). Regardless of its origin, the NAO can
induce changes in the trade winds and hence
the SSTAs in the tropical North Atlantic. Xie
and Tanimoto (1998) show that forced by theobserved wind variability in the extratropics, a
tropical coupled model can reproduce much of
the observed cross-equatorial SST gradientvariability, suggesting an extratropics-totropics forcing. In fact, the SLP signal associated the NAO propagates equatorward (Halliwell 1997) and then it could change the signof trade wind anomaly. Thus, the tropical SST
dipole, the ITCZ and the North Atlantic subtropical high are probably engaged in a twoway interaction between the tropics and extratropics. The interhemispheric PADO pattern inthis and other studies is most likely a result ofsuch an interaction.
7.

Summary

We have examined the time-space structuresof decadal variability in the Atlantic Ocean

1216

Journal of the Meteorological Society of Japan

using various observational datasets of marine
meteorological variables. We performed twopairs of EOF analyses separately for the Northand South Atlantic: one pair for SST and one
for SLP variability. While these two pairs of
sub-datasets form statistically independent
samples, the PCs of their respective leading
EOFs are significantly correlated. The same
EOF analysis has been repeated using a longer
record of SST observations for three different
periods of the 20th century. The results suggestthat an interhemispheric PADO pattern—which consists of a tripole in the North and adipole in the South Atlantic—exists throughout
the entire 20th century. The leading SST EOFs
separately obtained for the North and South
Atlantic form a meridional dipole across theequator, a SSTA configuration maximizing
cross-equatorial SST gradient anomalies. Given
the high sensitivity of the Atlantic ITCZ tocross-equatorial SST gradient, we suggest thatthe association of the tropical SST dipole with
the PADO is not accidental but is the centerpiece that links climate variability over thewhole Atlantic Ocean together. Our analysis
here and recent GCM studies (Okumura et al.
2001; Sutton et al. 2001; N.-C. Lau 2000, personal comm.) corroborate the association between the ITCZ and barotropic variability inthe subtropical North Atlantic. This barotropic
anomaly center contributes significantly to surface wind variability in the tropical North Atlantic.
Wind-induced latent heat flux is the major
forcing for the SST tripole in the North Atlantic, consistent with previous studies (Deser andBlackmon 1993; Seager et al. 2000; Okumura
et al. 2001; Lau and Nath 2001), with sensible
heat flux making significant contributions in
the high-latitudes. In the South Atlantic, bycontrast, COADS wind anomalies are not wellorganized spatially and do not even satisfy thegeostrophic balance with SLP anomalies. Our
comparison with satellite wind measurements
indicates that this poor spatial organization of
COADS wind variability in the South Atlantic
is due to the lack of ship observations there.
Assimilating SLP measurements, the NCEP
reanalysis yields improved the wind anomaly
pattern but its variance is still much weaker
than in the North Atlantic. The characteristics
of South Atlantic wind variability and its rela-Vol. 80, No. 5tion with SST are a subject of future investigations.
We have detected a spatially coherent cloud
anomaly pattern in both the North and South
Atlantic. In the off-equatorial tropics polewardof 10 , low-level cloud cover increases withdecreasing SST, and its shielding of solar radiation leads to a positive feedback between theclouds and SST, which can reduce SST damping rate by as much as 30%. Compared to thetropical North Atlantic, this cloud-SST feedback appears to be twice as strong in the South
Atlantic, helping maintain SST variability
there despite weak variability in COADS wind.
Our analysis suggests an interhemispheric
decadal mode that spans the whole Atlanticbasin from the southern tip of South America
to Greenland and appears to exist for the 20th
century. Proxy-based climate records in theequatorial Atlantic also indicate an existence of
this decadal mode prior to last century (Blacket al. 1999; Cobb et al. 2001). By no means,however, this implies that it is the only or eventhe dominant mode of Atlantic climate variability. In fact, this PADO mode accounts for
4–5% total unfiltered variance (40% in the 8–
16-year decadal band) over the whole Atlantic.
Atlantic variability is affected by many factors;
Pacific ENSO forcing (Enfield and Meyer 1997;
Chang et al. 2000; Lau and Nath 2001), stochastic forcing by the NAO (Xie and Tanimoto1998) and other atmospheric modes, the equatorial mode (Zebiak 1993; Carton et al. 1996)
and slow ocean dynamics (Eden and Willebrand 2001) are just some examples that cangive rise to variability other than the PADO
mode. The present study offers a view fromthe perspective of separate EOF analysis for
the North and South Atlantic. Empirical andmodeling studies from other angles are necessary to advance our understanding of Atlantic
variability. To achieve this goal, we call forenhanced observations of the South Atlantic,
both in-situ and by using new satellite remotesensing technology. Data archeology (e.g.,
Manabe 1999) and proxy-based paleoclimate
reconstruction are also highly desired.
Acknowledgment
The authors are grateful to Profs. T. Yamagata, H. Nakamura, N. Iwasaka and T. Matsuno for stimulating discussions. We also thankOctober 2002

Y. TANIMOTO and S.-P. XIE

Fig. A1. Normalized principal component(PC) of SST anomalies in the entire
Atlantic. This PC is calculated fromperforming EOF analysis for mostly
updated GISST anomalies from 1901–
2000.

the NOAA/NCEP for providing the reanalysis
data and the NCAR for providing the COADS/LMRF dataset. This work is supported by the
Frontier Research System for Global Change
and NOAA CLIVAR Atlantic Program throughgrant NA17RJ1230. The IPRC contribution#164 and SOEST #6009.
Appendix
Figure A1 represents mostly updated PADO
index from 1901 through 2000. This index
is calculated from performing the EOF analysis for updated GISST over the entire Atlantic.
The exact values over 100 years are available on ‘http://wwwoa.ees.hokudai.ac.jp/people/
tanimoto/’.
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