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Further

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Sea Surface Temperature
Variability: Patterns
and Mechanisms
Clara Deser,1 Michael A. Alexander,2 Shang-Ping Xie,3and Adam S. Phillips1
1

Climate Analysis Section, Climate and Global Dynamics Division, National Center for
Atmospheric Research, Boulder, Colorado 80305; email: cdeser@ucar.edu, asphilli@ucar.edu

2Earth System Research Laboratory, National Oceanic and Atmospheric Association, Boulder,
Colorado 80305; email: Michael.Alexander@noaa.gov

3

International Paciﬁc Research Center, School of Ocean and Earth Science and Technology,
University of Hawaii, Honolulu, Hawaii 96822; email: xie@hawaii.edu

Annu. Rev. Mar. Sci. 2010. 2:115–43

Key Words

First published online as a Review in Advance on
September 15, 2009

ocean-atmosphere interaction, El Niño, Paciﬁc Decadal Oscillation,
Atlantic Multidecadal Oscillation, climate variability

The Annual Review of Marine Science is online atmarine.annualreviews.org
This article’s doi:
10.1146/annurev-marine-120408-151453
c 2010 by Annual Reviews.
Copyright All rights reserved
1941-1405/10/0115-0115$20.00

Abstract
Patterns of sea surface temperature (SST) variability on interannual andlonger timescales result from a combination of atmospheric and oceanic
processes. These SST anomaly patterns may be due to intrinsic modes ofatmospheric circulation variability that imprint themselves upon the SST
ﬁeld mainly via surface energy ﬂuxes. Examples include SST ﬂuctuations in
the Southern Ocean associated with the Southern Annular Mode, a tripolar
pattern of SST anomalies in the North Atlantic associated with the North
Atlantic Oscillation, and a pan-Paciﬁc mode known as the Paciﬁc Decadal
Oscillation (with additional contributions from oceanic processes). They
may also result from coupled ocean-atmosphere interactions, such as the El
Niño-Southern Oscillation phenomenon in the tropical Indo-Paciﬁc, thetropical Atlantic Niño, and the cross-equatorial meridional modes in thetropical Paciﬁc and Atlantic. Finally, patterns of SST variability may arise
from intrinsic oceanic modes, notably the Atlantic Multidecadal Oscillation.

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1. INTRODUCTION

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The oceans play an important role in the climate system owing in part to their large heat-storage
capacity: Approximately 3.5 m of water contains as much energy as an entire atmospheric column.
The oceans’ thermal inertia is communicated to the atmosphere via turbulent and radiative energy
exchange at the sea surface. These energy ﬂuxes in turn depend on a single oceanic quantity, thesea surface temperature (SST), as well as several atmospheric parameters including wind speed,
air temperature, humidity, and cloudiness. SSTs thus play a key role in regulating climate and its
variability. In particular, slow variations in SST provide a source of potential predictability forclimate ﬂuctuations on timescales of seasons and longer. In this review, we describe the dominantspatial and temporal patterns of nonseasonal SST variability observed over the past ∼150 years
and discuss the current state of knowledge regarding their underlying physical mechanisms andclimate impacts.
The rest of the paper is outlined as follows: Section 2 provides some background on the physicalprocesses governing SST variability, including the role of atmospheric circulation forcing. The
North Atlantic SST tripole mode is used to illustrate the latter. Section 3 introduces the SST data
sets commonly used for climate studies, with an emphasis on issues related to spatial and temporal coverage. Section 4 discusses the geographical distribution of nonseasonal SST variability
and provides a brief introduction to Empirical Orthogonal Function analysis, a commonly usedtechnique for identifying patterns of variability. Section 5 presents the primary modes of nonseasonal SST variability in each ocean basin. These include the following (in addition to the North
Atlantic SST tripole): (a) the El Niño-Southern Oscillation (ENSO) phenomenon in the tropicalPaciﬁc, with teleconnections to the other ocean basins; (b) the tropical Indian Ocean dipole mode;(c) the tropical Atlantic Niño and meridional modes; (d ) Southern Ocean variability; (e) the Paciﬁc
Decadal Oscillation; and ( f ) the Atlantic Multidecadal Oscillation. Summary Points and Future
Issues sections conclude this article.

2. PHYSICAL BACKGROUND
2.1. Heat Budget of the Upper-Ocean Mixed Layer
SSTs are governed by both atmospheric and oceanic processes. On the atmospheric side, windspeed, air temperature, cloudiness, and humidity are the dominant factors regulating the exchangeof energy at the sea surface. On the oceanic side, heat transport by currents, vertical mixing, andboundary layer depth inﬂuence SST. Mathematically, the heat budget for the upper-ocean mixed
layer may be written as∂ T/∂t = Q net /(ρC p H) + (U geo + U ek ) · ∇T + (We + Wek )(T − Tb )/H,

(1)

where ρ is the density of seawater, Cp is the speciﬁc heat of seawater, H is the mixed layer depth,
T is the mixed layer temperature (equal to the SST), Qnet is the net surface energy ﬂux, Ugeo is
the geostrophic current velocity, Uek is the Ekman current velocity, We is the vertical entrainment
rate, Wek is the Ekman pumping velocity, and Tb is the temperature of the water at depth that is
entrained into the mixed layer. Qnet is deﬁned as
Q net = Q s h + Qlh + Q s w + Qlw ,(2)where Qsh is the sensible heat ﬂux, Qlh is the latent heat ﬂux, Q s w is the downward solar radiativeﬂux minus the portion that penetrates through the mixed layer, and Qlw is the longwave radiativeﬂux. The turbulent (Qsh + Qlh ) energy ﬂux is linearly proportional to the wind speed and the
air-sea temperature or humidity difference; the radiative ﬂuxes (Q s w + Qlw ) are functions of air
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temperature, humidity, and cloudiness. The bulk formulas for the air-sea ﬂuxes may be found in
standard texts such as Peixoto & Oort (1992), Pond & Pickard (1983), and Hartmann (1994). Ekman and geostrophic currents contribute to the heat budget of the mixed layer through horizontal
advection, whereas entrainment and Ekman pumping alter the SST through vertical advection. A
complete discussion of these terms may be found in standard oceanography texts such as Pond &Pickard (1983), Vallis (2006), and Stewart (2005).

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2.2. Atmospheric Circulation Forcing
Much of the large-scale organization of SST anomalies results from the large-scale organizationof atmospheric circulation anomalies and attendant changes in the turbulent and radiative energy
ﬂuxes at the air-sea interface and the local wind-driven Ekman currents (Cayan 1992, Alexander& Scott 1997, Deser & Timlin 1997, Marshall et al. 2001, Visbeck et al. 2003, Alexander & Scott2008). In the middle latitudes, eastward-moving storms dominate surface energy ﬂux variations
on synoptic timescales (3 to 10 days), whereas blocking events, slowly moving cutoff lows, Rossby
waves, and semistationary patterns contribute to surface energy ﬂux variations on timescales longer
than approximately 10 days (Alexander & Scott 1997). In general, atmospheric variability withtimescales longer than approximately 10 days is more effective at driving SST anomalies than issynoptic atmospheric variability, because of the thermal inertia of the upper-ocean mixed layer(Frankignoul & Hasselmann 1977, Deser et al. 2003). The frequency dependency of the thermal
response of the mixed layer to atmospheric forcing is discussed further below.
Extra-tropical atmospheric circulation variability on timescales longer than approximately
10 days tends to be organized into recurring large-scale patterns anchored to particular geographical regions. These large-scale patterns often consist of anomalies of opposite sign over distant parts of the globe, commonly referred to as teleconnections in the meteorological literature(Wallace & Gutzler 1981, Barnston & Livezey 1987, Kushnir & Wallace 1989, Trenberth et al.
1998). Prominent examples of large-scale teleconnection patterns of extratropical atmosphericcirculation variability include the North Atlantic Oscillation (NAO) and the related Northern
Annular Mode (NAM), the Paciﬁc-North American and Paciﬁc-South American patterns, andthe Southern Annular Mode (SAM). Nigam (2003) reviewed these and other atmospheric circulation teleconnection patterns.
The NAO refers to a redistribution of atmospheric mass, manifest as sea-level pressure (SLP)changes of opposite sign, between the Arctic and subtropics of the Atlantic sector (e.g., Figure 1).
This SLP oscillation (in space, not time: Walker & Bliss 1932) is accompanied by changes inwind, storminess, air temperature, and precipitation across the North Atlantic as well as overeastern North America and Europe (for a recent review, see Hurrell & Deser 2009). The related
NAM incorporates the spatial pattern of the NAO with a third center of SLP variability over the
North Paciﬁc that ﬂuctuates in phase with that over the North Atlantic. The NAM’s Southern
Hemisphere counterpart, the SAM, also refers to a north-south redistribution of atmospheric mass
between high (poleward of 50◦ S) and middle (30◦ –50◦ S) latitudes, with accompanying changes inwind, air temperature, and precipitation (Marshall 2003). The SAM is a zonally symmetric patternof atmospheric circulation variability that spans all longitudes, whereas the NAM exhibits more
zonal variation due to continental effects (Thompson & Wallace 2000). Finally, the Paciﬁc-North
American and Paciﬁc-South American are wave-like teleconnection patterns consisting of SLP
anomaly centers that approximately follow a great circle route over the Paciﬁc and American
sectors of their respective hemispheres (Wallace & Gutzler 1981, Karoly 1989). Most prominent
during winter, these patterns affect wind, air temperature, and precipitation over the North andSouth Paciﬁc.
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b

SST

QSH + QLH

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2 m s–1

–0.6

–0.4

–0.2

–0.05

0.05

0.2

0.4

0.6

2 m s–1

–35

–25

–15

Temperature (°C sd–1)

c

–5

5

15

25

35

(W m2 sd–1)

d

Ekman

QSH + QLH + Ekman

2 m s–1

–6

–4

–2

–0.5

0.5

(W m2 sd–1)

2

4

6

2 m s–1

–35

–25

–15

–5

5

15

25

35

(W m2 sd–1)

Figure 1
Anomaly patterns associated with a +1 standard deviation departure of the North Atlantic Oscillation (NAO) Index during winter(December–March) deﬁned using the station index of Hurrell et al. (2003). (a) Sea surface temperature (SST) (shading), sea-levelpressure (SLP) (contours), and surface wind (arrows). (b) Sensible plus latent energy ﬂux (Qsh + Qlh ) (shading), SLP (contours), and surface
wind (arrows). (c) Ekman heat transport expressed as an equivalent surface energy ﬂux (shading), long-term mean SST (contours), andEkman currents (arrows). (d ) Sum of the sensible, latent, and Ekman energy ﬂuxes (shading), SLP (contours), and surface wind (arrows).
The SLP contour interval is 1 hPa, with negative values dashed.

The spatial structures of these large-scale patterns of extratropical atmospheric circulation
variability are driven primarily by internal nonlinear dynamical processes. That is, they are intrinsic to the atmosphere and require no external forcing to exist. The temporal evolution of these
atmospheric circulation patterns is generally consistent with a stochastic ﬁrst-order autoregressive
process with an e-folding timescale of approximately 10 days (Feldstein 2000; N.C. Johnson &S.B. Feldstein, unpublished manuscript). That is, these patterns tend to follow a random sequenceakin to white noise on timescales longer than approximately 10 days. Although these teleconnection patterns are intrinsic to the atmosphere, they may also be excited by external forcing
factors such as changes in SSTs, sea ice, atmospheric chemical composition (for example, volcanic aerosols as well as ozone and greenhouse gas concentrations), and solar output, in analogy
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with the excitation of the “normal modes” of a drum. These external forcings may thus alter the
temporal evolution of atmospheric teleconnection patterns, enhancing their persistence and potential predictability. External forcing may also generate new patterns of atmospheric circulation
response.
In contrast to the extratropics, large-scale patterns of atmospheric circulation variability inthe tropics result primarily from interaction with the ocean: That is, they do not exist in theabsence of SST variability. The most prominent large-scale tropical atmospheric pattern of variability is the Southern Oscillation, an east-west redistribution of atmospheric mass between the
Indian Ocean/western Paciﬁc and the eastern Paciﬁc. In the positive (negative) phase of theSouthern Oscillation, the SLP gradient across the tropical Paciﬁc is stronger (weaker) than normal, accompanied by enhanced (diminished) trade winds and equatorial easterly winds. Thispattern owes its existence to tropical ocean-atmosphere interaction associated with the ENSO
phenomenon and is thus an intrinsically coupled phenomenon. The roles of intrinsic and oceancoupled atmospheric variability are discussed further in connection with speciﬁc patterns of SST
variability.
The large-scale nature of atmospheric teleconnection patterns is imprinted upon the SST
anomaly ﬁeld via the surface energy ﬂuxes and Ekman currents. This is illustrated for the NAO
in Figure 1. The SST ﬁeld exhibits a tripolar structure marked by a cold anomaly in the subpolar
North Atlantic, a warm anomaly in the middle latitudes, and a cold subtropical anomaly between
the equator and 30◦ N; warm anomalies are also found in the Greenland and North Seas. Maximum SST anomaly amplitudes are on the order of 0.5◦ C. A similar tripolar pattern is seen in
the turbulent energy ﬂuxes (Figure 1b), where the sign is deﬁned as positive downwards (e.g.,
from the atmosphere to the ocean). The ocean loses energy to the atmosphere over the subpolar
and tropical Atlantic as a result of strengthened westerly winds and northeast trade winds, respectively. It gains energy in the middle latitudes as a result of the diminished wind speeds (easterly
anomalies superimposed on a background of mean westerlies) and anomalous warm-air advection associated with southerly wind anomalies along the eastern seaboard of the United States.
Thus, the large-scale structure of the SST anomaly tripole is driven by the turbulent energy ﬂux
anomalies associated with the NAO (e.g., Cayan 1992, Marshall et al. 2001, Visbeck et al. 2003).
Whereas the mixed-layer heat-budget equation relates the ﬂuxes to the time tendency of SST andnot the SST per se (recall Equation 1), the winter-mean SST anomaly closely resembles the SST
anomaly tendency across the winter season and the SST anomalies lag the atmospheric forcing by
2–3 weeks (e.g., Deser & Timlin 1997).
Although the surface turbulent energy ﬂuxes drive the overall tripolar pattern of SST anomalies
associated with the NAO, horizontal temperature advection by the anomalous Ekman currents,
expressed as an equivalent surface heat ﬂux anomaly, also contributes in regions of strong mean
SST gradients such as the Gulf Stream and North Atlantic Current (Figure 1c). Other factors
such as the radiative ﬂuxes at lower latitudes and spatial variations in mixed-layer depth also
play a role in determining the magnitude of the SST anomalies. For example, the winter mixed
layers are substantially deeper in the subpolar North Atlantic compared with the tropical Atlantic(∼300 m versus ∼60 m), which reduces the amplitude of the SST response (recall Equation 1). Thiseffect is evidenced by the fact that the NAO-related SST anomalies are of comparable magnitude
between high and low latitudes, whereas the heat ﬂux anomalies differ by approximately a factor of
5 (Figure 1). Although the SST anomalies associated with the North Atlantic tripole are primarily
forced by the atmosphere, there is some evidence for a weak feedback on the NAO (Watanabe &Kimoto 2000, Czaja & Frankignoul 2002).
An important concept for understanding the nature of SST variability is the stochastic climate model paradigm (Frankignoul & Hasselmann 1977). According to this paradigm, oceanwww.annualreviews.org • Sea Surface Temperature Variability

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mixed-layer temperature anomalies are forced by random atmospheric variability (e.g., atmospheric circulation variability that decorrelates within a week or two) via surface energy ﬂuxes andEkman currents, and decay by damping back to the atmosphere via turbulent energy and longwave
radiative ﬂuxes (modeled as a negative linear feedback term). Put another way, the ocean mixed
layer integrates the “white noise” atmospheric forcing to yield a “red noise” SST response. In thisview, the predictability or persistence of SST anomalies is limited to the timescale associated withthe thermal inertia of the mixed layer, a timescale determined by the depth of the mixed layer
and the rate at which the SST anomaly damps to the atmosphere via turbulent heat ﬂuxes. The
simple stochastic climate model has been widely adopted as the leading paradigm for the “nullhypothesis” of SST variability in middle and high latitudes where random atmospheric forcing isa good approximation.
An illustration of the simple stochastic climate model paradigm is given in Figure 2, whichshows the mixed-layer temperature response to random variations in surface heat ﬂux forcing overa 60-year period. Two cases are considered: a shallow (50 m) and a deep (500 m) mixed layer. These
values span the typical range of observed mixed-layer depths for which 50 m is representative ofannual mean conditions in the North Paciﬁc and 500 m typical of the northern North Atlantic
during winter. Slow variations in the mixed-layer temperature are apparent for both cases, with
ﬂuctuations on the order of a few years for the shallow case and on the order of decades for theAnnu. Rev. Marine. Sci. 2010.2:115-143. Downloaded from arjournals.annualreviews.org
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Forcing (W m–2)

W m–2

60

30

0
0

20

40

60

20

40

60

40

60

0.4

°C

SST (H = 50 m)
0

–0.4
0
0.1

°C

SST (H = 500 m)
0

–0.1
0

20

Time (years)
Figure 2
Illustration of the stochastic climate model paradigm showing a random (e.g., “white noise”) atmosphericheat ﬂux forcing time series (top graph) and the upper-ocean mixed-layer temperature response for amixed-layer depth of 75 m (middle graph) and 500 m (bottom graph). Note the slow ﬂuctuations in the ocean
mixed-layer temperature response.

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deep mixed-layer case. This simple example illustrates that slow SST variations may be induced byrandom (e.g., unpredictable) atmospheric forcing simply due to the thermal inertia associated witha deep mixed layer. Note that the stochastic climate model does not generate spectral peaks; rather,it enhances the low-frequency component of variability according to a ﬁrst-order autoregressive
process. The stochastic climate model paradigm contributes to SST variability in the North
Paciﬁc associated with the Paciﬁc Decadal Oscillation (Newman et al. 2003) and to the long(decadal timescale) persistence of SST ﬂuctuations in the North Atlantic (Deser et al. 2003,
DeCoetlogon & Frankignoul 2003). This powerful null hypothesis of SST variability makes it
difﬁcult to separate the contributions of oceanic and atmospheric forcing solely on a timescalebasis.
In addition to atmospheric processes, oceanic processes such as upwelling, entrainment, and lateral advection also contribute to SST variability. For example, vertical advection plays a prominentrole along the coastal and equatorial upwelling zones, and horizontal advection is important along
the western-boundary current regions (e.g., the Gulf Stream and Kuroshio Current). Oceanic processes also play an indirect role in SST variability by affecting the depth of the upper-ocean mixed
layer. Even the mean seasonal cycle of mixed-layer depth can induce winter-to-winter memoryor persistence of SST anomalies via the “re-emergence” mechanism whereby thermal anomalies
stored in the deep winter mixed layer persist at depth through summer and become partiallyreentrained into the mixed layer during the following winter (Namias & Born 1974, Alexander &Deser 1995, Deser et al. 2003). Several of these processes have been incorporated to extend theframework of the stochastic climate model, including the re-emergence mechanism (Deser et al.
2003) and horizontal advection (Frankignoul & Reynolds 1983, DeCoetlogon & Frankignoul
2003).

3. SEA SURFACE TEMPERATURE DATA SETS
The patterns of variability sampled in any particular SST data set will depend to some extent on
the spatial and temporal resolution of the data and the length of record. For example, satellitebased SST archives of high spatial resolution but short duration may reveal patterns with enhanced
meso-scale deﬁnition but lack information on multidecadal variability. Conversely, historical shipbased archives of coarse and uneven spatial sampling but long duration may reveal patterns withtimescales of multiple decades and longer but lack detailed spatial information. Here we provide abrief overview of the most commonly used SST data sets in climate studies, including informationon their spatial resolution and temporal coverage as well as any ﬁlling procedures used to accountfor missing data. Our discussion is limited to those data sets that are available on a regular grid
over the world oceans (Table 1).
The International Comprehensive Atmosphere-Ocean Data Set (ICOADS) is an extensive andwidely used digital collection of quality-controlled surface weather observations (including SST).
The majority of the measurements come from ships of opportunity, supplemented in recent years
by research vessels, moored environmental buoys, drifting buoys, and near-surface measurementsfrom hydrographic proﬁles. The data are monthly averaged and binned into 2◦ latitude by 2◦
longitude boxes beginning in 1800 (1◦ by 1◦ beginning in 1960) and extending through 2007 with
updates every few years.
Due to the uneven distribution of commercial shipping routes and changes in those routes
over time, data coverage is poor in certain regions and periods (Figure 3). Broadly speaking, theNorth Atlantic, western South Atlantic, and northern Indian oceans contain the highest densityof observations, with reasonable coverage back to approximately 1870. Data coverage is limited

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Commonly used SST data sets for climate studies

Name and Web address

Period of record

Coverage

ICOADS http://icoads.noaa.gov/

1800–present

Monthly2◦ × 2◦ since 1800
1◦ × 1◦ since 1960

HadISST

1870–present

http://badc.nerc.ac.uk/data/hadisst/
NCDC

1854–present

http://www.ncdc.noaa.gov/oa/climate/research/sst/
ersstv3.php

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NOAA Optimum Interpolation SST Analysis

Nov. 1981–present
Sept. 1981–present

http://ingrid.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/

Monthly

In situ/satellite data

1◦ × 1 ◦Missing values ﬁlled

Monthly

In situ/satellite data2◦ × 2 ◦Missing values ﬁlled

Weekly/monthly

Satellite estimates

Daily

Satellite estimates

0.25◦ × 0.25◦

Analysis
http://www.ncdc.noaa.gov/oa/climate/research/sst/oidaily.php
Kaplan Extended v2 SST Anomaly Data

No processing
beyond quality
control

1◦ × 1 ◦http://www.emc.ncep.noaa.gov/research/cmb/sst analysis/
NOAA Optimum Interpolation 1/4 Degree Daily SST

Comments
In situ data

1856–present

Monthly

In situ/satellite data

5◦ × 5 ◦Missing values ﬁlled

in the North Paciﬁc before around 1946 and in the tropics before around 1960; the Southern
Ocean remains poorly sampled throughout the record. The uneven and changing spatial coverageof SST measurements from historical ship-based archives must be taken into account in any
analyses.
Individual SST measurements in the ICOADS are subjected to quality-control procedures toremove outliers and duplicates. However, the screened values are not corrected for changes in
instrumentation, observing practice, ship type, etc.; missing grid boxes are not ﬁlled in; and no“analysis” of the data is performed (e.g., no spatial or temporal smoothing or interpolation). An
example of errors introduced by changes in instrumentation is the spurious warming trend around1941 as a result of switching from bucket to engine-intake measurements (Thompson et al. 2008).
The lack of spatial and temporal smoothing in the ICOADS, along with large uncertainties inindividual monthly mean values due to inadequate sampling, makes it difﬁcult to produce comprehensible maps of a particular climatic variable for a speciﬁc month and year without additional
processing of the data.
Various empirical and statistically optimal procedures have been employed to improve upon
the sampling uncertainties, temporal inhomogeneities, and missing data in the ICOADS and related archives. These globally complete monthly SST analysis products (e.g., HadISST, Kaplan,and NCDC in Table 1) are tremendously useful for certain applications, for example, in studies of global SST variability and as lower-boundary conditions in atmospheric model simulations,although they remain constrained by the quality, quantity, and distribution of the original measurements (c.f., Hurrell & Trenberth 1999). The HadISST data are reconstructed using a two-stage
reduced-space optimal interpolation procedure, followed by superposition of quality-improved
gridded observations onto the reconstructions to restore local detail (Rayner et al. 2003). Similar
methods are used for Kaplan and NCDC (see Web addresses in Table 1 for detailed information).
In this review, we make use of the HadISST data set in part because of the higher spatial resolution

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1860–1879

1880–1899

1900–1919

1920–1939

1940–1959

1960–1979

1

10

20

30

40

50

60

70

80

90

99

%

Figure 3
Distribution of sea surface temperature observations from the International Comprehensive Ocean
Atmosphere Data Set for each 20-year period since 1860. Color shading indicates the percentage of months
with at least one measurement in a 2◦ latitude by 2◦ longitude grid box.

(1◦ latitude by 1◦ longitude) compared with the Kaplan (5◦ latitude by 5◦ longitude) and NCDC
(2◦ latitude by 2◦ longitude) archives.
With the advent of satellite retrievals of SST beginning in the early 1980s, there is now full
SST coverage over the world oceans at high temporal (every few days) and spatial (∼10–25 km)
resolution. Various satellite retrieval methods are available, including infrared sensors from theAdvanced Very High Resolution Radiometer and microwave measurements from the Advanced
Microwave Scanning Radiometer. The satellite data have also been blended with conventional in

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situ data to account for biases and inhomogenieties associated with changing satellites and orbits
through time (NOAA Optimum Interpolation products; see Table 1).

4. GEOGRAPHICAL PATTERNS OF NONSEASONAL SEA SURFACETEMPERATURE VARIABILITY
4.1. Standard Deviations

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A common approach to quantifying the magnitude and spatial distribution of nonseasonal SST
variability is to map the standard deviation (σ) of the monthly SST anomalies (deﬁned as thedeparture of the SST for a particular month from the long-term monthly mean) at each location.
Figure 4b shows the σ distribution based on satellite-derived SST values during 1982–2008. The
long-term mean SST distribution is also shown for reference (Figure 4a). Three regions standout as having the largest nonseasonal variability: the upwelling zones along the equatorial Paciﬁc
and coastal Peru, the western-boundary current regions of the Kuroshio and Gulf Stream, and theSouthern Ocean from the tip of South Africa eastward into the Indian Ocean. Maximum valuesof the SST anomaly standard deviations in these regions exceed 1.5◦ C. In the extratropics, theregions of maximum SST anomaly standard deviation coincide with regions of maximum northsouth mean SST gradients (termed frontal zones). In the tropics, the highest standard deviations
occur where there is a local minimum in the long-term mean SSTs due to equatorial and coastal
upwelling. Apart from the regions of maximum SST anomaly standard deviation, there is relatively
high variability (standard deviations around 0.75◦ C–1◦ C) across the North Paciﬁc and North
Atlantic and the South Paciﬁc, South Indian, and South Atlantic and relatively low variability
across the tropical ocean basins (standard deviations around 0.3◦ C–0.5◦ C). A similar map basedon the COADS archive lacks the sharpness of the SST variance maxima seen in the satellite dataowing to the coarser sampling in both space and time.

4.2. Methods for Identifying Patterns of Variability
The standard deviation map of nonseasonal SST anomalies presented in Figure 4 does not give
any information on how SST variations at one location are related to those at another. To ascertain
the covariability of SST anomalies at different locations, alternative analysis methods are needed.
One commonly used approach is empirical orthogonal function (EOF) analysis and extensions
thereof (von Storch & Zwiers 1999). Conceptually, EOF analysis determines a spatio-temporal
pattern of variability that accounts for the maximum covariance between the SST anomaly timeseries at all pairs of grid points in the data set. Then, the remaining covariability is subject tothe same decomposition with the added constraint that the second spatio-temporal EOF pattern is orthogonal (e.g., uncorrelated) in both time and space to the leading EOF pattern. This
procedure is repeated until all n EOF patterns have been computed, where n is equal to thenumber of grid points. In practice, only the ﬁrst few leading modes are robust as a result of theorthogonality constraint. EOF rotation may be used to circumvent this constraint; however, it
may also result in patterns that are overly localized in space. Each EOF pattern is associatedwith a principal component (PC) time series, which describes the temporal evolution of the EOF
pattern. The PC time series may be obtained by projecting the EOF pattern onto the original
SST anomaly ﬁeld at each time step so as to ﬁnd the sign and amplitude of the pattern at any
given time. It should be noted that the sign of the EOF is arbitrary; however, the product of the
EOF and the PC time series recovers the correct polarity of the mode at any given grid box and
time.
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Climatology

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Figure 4(a) Long-term mean sea surface temperature (SST) distribution from satellite passive microwave
measurements during 1982–2008. (b) The standard deviation of monthly SST anomalies (deviations fromthe long-term monthly means) based on the same data set.

Although useful, EOF analysis is not foolproof. The EOF modes depend on the spatial domain
considered, are subject to orthogonality constraints, and may not be separable if they accountfor similar percentages of the total variance (North et al. 1982). Also, EOFs are empiricallydetermined “modes” and thus are not necessarily equivalent to the dynamical modes of the system. It is always prudent to conﬁrm EOF patterns with simpler techniques such as compositing,
one-point correlation maps, and linear regression analysis. Some examples are given in the next
section.
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5. DOMINANT PATTERNS OF NONSEASONAL SEA SURFACETEMPERATURE VARIABILITY
We ﬁrst describe the dominant patterns of SST variability in the tropics, followed by those in theextra tropics.

5.1. Tropics
In this section, we consider ENSO in the Paciﬁc, the basin-wide and zonal dipole modes in the
Indian Ocean, and the Niño and meridional dipole modes in the Atlantic.

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5.1.1. ENSO. The leading EOF of monthly SST anomalies over the globe is the ENSO mode.
Here we show the leading EOF based on linearly detrended monthly anomalies during the period
1900–2008 from the HadISST data set (Figure 5a), but similar results are found for shorter time
periods and other SST data sets. This single mode accounts for 19% of the nonseasonal variabilityof SSTs over the global oceans during the past 109 years. The spatial pattern associated withthe warm phase of ENSO consists of positive SST anomalies across the eastern two-thirds ofthe equatorial Paciﬁc Ocean, ﬂanked by weaker negative anomalies over the far western tropicalPaciﬁc and extending in a horseshoe-like fashion to the North and South Paciﬁc. Positive SST
anomalies are also found along the west coasts of North and South America. ENSO-related SST
anomalies also occur over the Atlantic, Indian, and Southern oceans.
A commonly used index for representing SST variability associated with ENSO is the area
average of monthly SST anomalies in the region 5◦ N–5◦ S, 170◦ –120◦ W (outlined by the box in
Figure 5a), referred to as the Niño-3.4 SST index for historical reasons having to do with the
conﬁguration of ship tracks in the region (Rasmusson & Carpenter 1982). The Niño-3.4 SST
index is nearly identical to the PC time series associated with the leading EOF: Their correlation
coefﬁcient is 0.93. The Niño-3.4 time series exhibits an irregular series of warm and cold “events”
typically lasting about 1–1.5 years and recurring approximately every 3–8 years (Figure 5b). The
largest warm events such as those that occurred in 1982–1983 and 1997–1998 have amplitudes
of approximately 2◦ –2.5◦ C, whereas more moderate warm events range from 1◦ C to 2◦ C. Cold
events tend to be somewhat weaker and longer lasting than their warm counterparts. A power
spectrum of the Niño-3.4 time series shows that the dominant range of periods is 2.5–8 years,
with some sensitivity to the period of record sampled (Figure 5c). ENSO is seasonally dependent
as indicated by the seasonal cycle of the standard deviation of the Niño-3.4 SST anomaly timeseries (Figure 5d ), which shows minimum values (∼0.55◦ C) during April–June and maximum
values (∼0.95◦ C) near the end of the calendar year (November–January).
ENSO-related SST anomalies evolve substantially during the course of a warm or cold event.
A composite ENSO evolution over a period of six seasons is shown in Figure 6, starting withconditions during March–May before the peak and ending with conditions in June–August of
the following year. The warm-event composite is based on a selection of the years for which theNiño-3.4 SST index in September–November exceeds 1 standard deviation based on the period
1950–2008. Similarly, the cold-event composite is based on a selection of the years for which theSeptember–November Niño-3.4 SST index is less than -1 standard deviation. The cold-event
composite is then subtracted from the warm-event composite. The composite ENSO evolution
is depicted for linearly detrended SST and SLP anomalies in Figure 6. Note that this approachis independent of the EOF analysis shown earlier.
The ENSO composite SST evolution exhibits an incipient warming along the equator andcoast of South America during MAM0 that subsequently grows in amplitude over the next several
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Figure 5(a) Leading empirical orthogonal function (EOF) of detrended monthly sea surface temperature (SST)
anomalies over the global oceans based on the HadISST data set during 1900–2008. This mode, which
accounts for 19% of the variance, depicts the El Niño-Southern Oscillation phenomenon. (b) Monthly SST
anomaly time series in the Niño-3.4 region (5◦ N–5◦ S, 170◦ –120◦ W: outlined by the rectangle on the EOF
pattern). (c) Power spectrum of the Niño-3.4 SST anomaly time series based on 1950–2008 (solid curve) and
1900–2008 (dashed curve). (d ) Monthly standard deviation of the Niño-3.4 SST anomaly time series.
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Figure 6
Composite ENSO evolution of seasonal sea surface temperature (shading) and sea-level pressure (SLP) (contours) anomalies starting inMarch–May of year 0 (MAM0) and ending with June–August of the following year ( JJA1). The SLP contour interval is 1 hPa, withnegative values dashed.

seasons, reaching its peak around SON0 and DJF1. (Here, the ﬁrst year of the ENSO event
is denoted by a zero and the subsequent year by a one.) The warm event diminishes during
MAM1 and begins to transition to a weak cold event in JJA1. Accompanying the warming is thesimultaneous development of negative SST anomalies in the northwest and southwest tropicalPaciﬁc. The tropical Indian Ocean as well as portions of the tropical Atlantic exhibit a delayed
warming relative to that in the Paciﬁc, reaching peak strength around DJF1 and MAM1 but
lingering through JJA1 and beyond. Similar results are shown in Harrison & Larkin (1998),
Rasmusson & Carpenter (1982), Trenberth et al. (2002), and Klein et al. (1999), among others.
Tropical SLP anomalies develop in tandem with the SST anomalies, reaching maximum strengthin DJF1. This large-scale SLP anomaly pattern, with positive values west of the International
Date Line and negative values to the east, is associated with the negative phase of the Southern
Oscillation (see also Deser & Wallace 1990, Harrison & Larkin 1996).
Not all ENSO events follow the canonical evolution depicted in Figure 6. For example, someevents develop mainly in the central equatorial Paciﬁc while others are conﬁned to the coastof South America (Deser & Wallace 1987, Kao & Yu 2009). In addition, some events show an
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eastward spreading of the equatorial Paciﬁc SST anomalies (Guan & Nigam 2008) in contrast to
the westward development of the composite ENSO.
ENSO is understood to be a natural mode of variability of the coupled ocean-atmospheresystem in the tropical Paciﬁc. Over the past 25 years, two broad paradigms have emerged to explain
the governing dynamics of ENSO. One paradigm is the delayed oscillator or recharge oscillator
theory, which holds that ENSO is an unstable mode of the coupled tropical ocean-atmospheresystem exhibiting self-sustained, regular oscillations (Zebiak & Cane 1987, Battisti 1988, Schopf& Suarez 1988). Irregularity in the ENSO period can occur through stochastic forcing (noise) in
the form of energetic weather events such as the Madden-and-Julian Oscillation and westerly windbursts or through low-order chaos (Munnich et al. 1991, Jin et al. 1994, Blanke et al. 1997). The
other leading paradigm is the stochastic forcing theory, which holds that ENSO is a damped stablemode of the system requiring energy from stochastic forcing to maintain the oscillation (Penland& Sardeshmukh 1995, Chang et al. 1996, Moore & Kleeman 1999, Thompson & Battisti 2001).
Regardless of the precise nature of the ENSO mode, a series of positive and negative feedbacks
between the atmosphere and ocean within the tropical Paciﬁc lead to the growth and decay ofan event. The dominant positive feedback is the so-called Bjerknes feedback between the wind
anomalies in the western and central equatorial Paciﬁc and the SST anomalies in the eastern Paciﬁc.
According to the Bjerknes feedback mechanism, westerly wind anomalies produce positive SST
anomalies through downwelling equatorial oceanic Kelvin waves, and these SST anomalies in turnweaken the zonal SLP gradient, which leads to westerly wind anomalies. The dominant negative
feedback involves the dynamical adjustment of the equatorial Paciﬁc thermocline to the overlying
wind ﬁeld with a time delay that eventually leads to the demise of an event. Much progress has
been made on the predictability of ENSO, leading to the routine issuance of ENSO forecasts(e.g., Palmer et al. 2004).

5.1.2. ENSO teleconnections: the atmospheric bridge. While air-sea interactions responsible for ENSO are centered over the equatorial Paciﬁc Ocean, changes in tropical precipitationfrom deep convection associated with ENSO inﬂuence the global atmospheric circulation, in partthrough the excitation of Rossby waves that emanate into the extra tropics of both hemispheres.
The wave energy tends to follow great circle routes, initially extending poleward and eastward,
refracting (turning) away from the pole and returning to the tropics. These planetary waves form
in preferred locations, which include high pressure in the subtropics and low pressure in midlatitudes of the North and South Paciﬁc. The overall structure of the ENSO response is inﬂuenced
by additional factors, including the detailed structure of the climatological wind ﬁeld that governs
the path of the wave energy, sources/sinks of Rossby waves outside of the tropical Paciﬁc, andchanges in the storm tracks (see reviews by Trenberth et al. 1998, Liu & Alexander 2007). The
ENSO-driven circulation patterns alter the near-surface air temperature, humidity, and wind as
well as the distribution of clouds far from the equatorial Paciﬁc. The resulting variations in the surface heat, momentum, and freshwater ﬂuxes induce changes in SST as well as salinity, mixed-layer
depth, and ocean currents. Thus, the atmosphere acts as a bridge spanning from the equatorial
Paciﬁc to the remainder of the global oceans.
Atmospheric teleconnections to the North Paciﬁc peak during DJF1, manifest by large amplitude negative SLP anomalies centered over the Gulf of Alaska (Figure 6). This pattern, thesurface expression of the Paciﬁc-North American, results in anomalous northwesterly winds thatadvect relatively cold dry air over the western/central North Paciﬁc, anomalous southerly windsthat advect warm moist air along the west coast of North America, and enhanced surface westerlies over the central North Paciﬁc. The resulting anomalous surface heat ﬂuxes and Ekman
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transport create negative SST anomalies in the central and western North Paciﬁc and positive
SST anomalies along the west coast of North America (Alexander et al. 2002).
The wintertime ENSO response also impacts the North Atlantic (Figure 6). During El Niñoevents, a pattern similar but not identical to the NAO develops in DJF1, with anomalous high (low)pressure in high (mid) latitudes in conjunction with a southward displacement and intensiﬁcationof the storm track along the eastern seaboard. This change in the atmospheric circulation createsa large-scale pattern of SST anomalies through the mechanisms discussed in Section 2. The SST
response most closely resembles the tripole pattern in MAM1, with negative anomalies in midlatitudes ﬂanked by positive anomalies in the subarctic and subtropics. Observational studies andmodel experiments suggest that the ENSO teleconnection over the North Atlantic is stronger andmore robust during La Niña events compared with El Niño events (Pozo-Vazquez et al. 2001).
ENSO-driven atmospheric teleconnections to the Southern Hemisphere are in many ways
symmetric to those in the Northern Hemisphere (Figure 6). For example, negative SLP anomalies
develop over the South Paciﬁc in austral winter ( JJA0), in analogy with those over the North Paciﬁcin boreal winter (DJF1). During austral summer (DJF1), the remote response to ENSO includes
not only a wave-like response but also a component that projects onto the SAM, with negative
SLP anomalies over mid-latitudes and positive SLP anomalies over high latitudes. The Southern
Hemisphere SST response to these atmospheric circulation anomalies includes negative anomaliesat ∼30◦ S and positive anomalies near the Antarctic Peninsula during El Niño events. These SST
anomalies are primarily driven by surface heat ﬂuxes in mid-latitudes and Ekman transport inhigh latitudes in both summer and winter (Ciasto & Thompson 2008). The amplitude of the SST
anomalies is largest during austral summer, when the mixed layer is shallow and thus the ocean
can rapidly respond to thermal forcing.
As mentioned above, the tropical Indian Ocean warms following an El Niño event, with SST
anomalies that lag those in the central Paciﬁc by approximately 3–6 months. Part of the basinwide warming is generated by the diminished wind speeds, which reduce the upward latent heat
ﬂux, and by the reduced cloudiness, which allows more solar radiation to reach the surface (Klein
et al. 1999). Ocean dynamics also inﬂuences El Niño–related SST anomalies in parts of the Indian
Ocean: Westward-propagating oceanic Rossby waves generated by anomalous wind-stress curl inthe southern Indian Ocean contribute by depressing the relatively shallow thermocline at ∼10◦ S(Xie et al. 2002). The slow propagation of the oceanic Rossby waves maintains the Indian Ocean
SST anomalies through JJA1, with associated atmospheric teleconnections to the subtropicalNorthwest Paciﬁc and East Asia (Xie et al. 2009).

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5.1.3. Non-ENSO variability in the tropical Indian and Atlantic oceans. The two leading
EOFs of detrended monthly SST anomalies over the tropical Indian Ocean (20◦ N–20◦ S) are
the basin-wide mode and the east-west dipole mode (Figure 7). The former is closely linked to
ENSO as described in the previous section, whereas the latter occurs both in conjunction with andindependently of ENSO. The two modes differ not only in their spatial expression, but also in theirseasonal dependency: The basin-wide mode peaks in January–March, whereas the dipole mode
peaks in July–October (Figure 7). The positive phase of the dipole mode exhibits weak positive
SST anomalies over the western two-thirds of the basin and strong negative SST anomalies inthe vicinity of Indonesia. A positive dipole mode is evident in the ENSO composite during SON0
(Figure 6).
Bjerknes feedback appears to contribute to the development of the dipole mode: The easterncooling induces easterly wind anomalies, which in turn drive equatorial upwelling that shoals
the thermocline, thereby reinforcing the SST cooling (Saji et al. 1999). The east-west seesaw
in the thermocline depth is part of the transient adjustment in response to wind variability near
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EOF1

EOF2

39%

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Temperature (°C sd–1)
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PC2 (EOF2)

0.6
0.3
0
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Month

Figure 7(a) First and second empirical orthogonal functions (EOFs) of detrended monthly SST anomalies in thetropical Indian Ocean based on the HadISST data set during 1900–2008. These modes account for 39% and
12% of the variance, respectively. EOF1 depicts the basin-wide mode and EOF2 depicts the dipole mode.
(b) Monthly standard deviations of the principal component time series associated with EOF1 (dark graycolumns) and EOF2 (light gray columns).

the equator. In fact, the dipole mode stands out as the leading mode in EOF analysis of thermocline
depth variability (Shinoda et al. 2004). The SST dipole mode is strongest during July–November
when upwelling-favorable southeasterly alongshore winds prevail off the coast of Indonesia, and
it is linked to a similar dipole in precipitation (negative in the east and positive in the west during
the positive phase). See Schott et al. (2009) for a recent review of Indian Ocean climate variability.
Predictability of the Indian Ocean dipole mode is discussed in Luo et al. (2007).
There are two major modes of tropical Atlantic variability: an equatorial mode akin to the
Paciﬁc El Niño and a meridional mode characterized by cross-equatorial SST anomaly gradients(a similar mode is also found in the tropical Paciﬁc). Recent reviews of these modes are provided
in Xie & Carton (2004) and Chang et al. (2006).
The equatorial Atlantic cold tongue displays signiﬁcant year to year variability, with typical
amplitude ∼0.5◦ C and frequency ∼2–3 years, although the latter is dependent on the time period
analyzed. This equatorial mode, called the Atlantic Niño, appears as the leading mode of tropicalAtlantic variability with strongest amplitude during May–July (Figure 8). It is similar to ENSO
in that ocean-atmospheric interactions give rise to positive feedbacks similar to those envisioned
by Bjerknes for ENSO in the Paciﬁc. The anomalous warming in the eastern equatorial Atlantic
relaxes the easterly trade winds, which reinforce the eastern warming by weakening the equatorial
upwelling and lowering the thermocline (Zebiak 1993). The Atlantic Niño intensiﬁes atmospheric
convection near the equator, and it keeps the summer rain band from advancing into the Sahel
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EOF1

EOF2

38%

20°N

20°N

10°N

10°N

0°

0°

10°S

10°S

20°S

25%

20°S
60°W

30°W

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Temperature (°C sd–1)
1.5
1.2
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PC1 (EOF1)
PC2 (EOF2)

0.6
0.3
0
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Month
Figure 8(a) First and second empirical orthogonal functions (EOFs) of detrended monthly sea surface temperature anomalies in the tropicalAtlantic Ocean based on the HadISST data set during 1900–2008. These modes account for 38% and 25% of the variance, respectively.
EOF1 depicts the Atlantic Niño mode and EOF2 depicts the meridional mode. (b) Monthly standard deviations of the principal
component time series associated with EOF1 (dark gray columns) and EOF2 (light gray columns).

while increasing rainfall at the Gulf of Guinea coast (Giannini et al. 2003). The Atlantic Niño isweaker and higher in frequency than its Paciﬁc counterpart.
The second EOF mode of tropical Atlantic SST variability shows a meridional dipole patternwith the nodal line displaced slightly north of the equator (Figure 8). The warm (cool) pole isassociated with the relaxed (intensiﬁed) easterly trades (Nobre & Shukla 1996). Such a wind-SST
relation suggests the following positive feedback via surface evaporation (Chang et al. 1997). An
initial SST dipole induces southerly winds across the equator. The Coriolis force causes thesesoutherlies to gain a westerly (easterly) component in the warm (cool) hemisphere, decelerating(accelerating) the prevailing easterly trades. The resultant changes in surface evaporation act
to amplify the initial SST dipole. This wind-evaporation-SST feedback preferentially ampliﬁes
equatorial-antisymmetric disturbances while the Bjerknes feedback grows east-west variations on
the equator. A similar phenomenon exists in the Paciﬁc (Chiang & Vimont 2004), with growing
evidence that it may play an important role in initiating ENSO events (Chang et al. 2007).
The meridional mode is most pronounced during the equatorial warm season March-May,with SST anomalies typically ∼0.5◦ C in the subtropics. The attendant SST gradients across theequator are very effective in displacing the Atlantic intertropical convergence zone (ITCZ) into
the warmer hemisphere. Rainfall in the semiarid Nordeste region of Brazil is sensitive to thismeridional mode and the ITCZ shifts it induces (Nobre & Shukla 1996). The meridional mode
also affects North Atlantic hurricanes through its inﬂuence on SST in the main development
region (90–20◦ W, 5–25◦ N) (Wang et al. 2006, Vimont & Kossin 2007).
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Besides wind-evaporation-SST feedback, there are several other factors for variability in the
cross-equatorial SST gradient. For example, ENSO affects SSTs over the tropical North Atlantic(recall Figure 6), whereas the North Atlantic Oscillation forces the same region by changing thenortheast trade winds (recall Figure 1) (see also Xie & Tanimoto 1998, Czaja et al. 2002). Theseand other factors result in a weak correlation between subtropical SST anomalies north and southof the equator. It has been suggested that the North Atlantic tripole (Deser & Blackmon 1993) anda South Atlantic dipole (Venegas et al. 1997) are correlated to form a pan-Atlantic pattern throughthe coordination of wind-evaporation-SST feedback across the equator (Xie & Tanimoto 1998).

In this section, we consider the dominant patterns of SST variability in the Southern Hemisphere,
the Paciﬁc Decadal Oscillation, and the Atlantic Multidecadal Oscillation.
5.2.1. Southern Hemisphere. Given the paucity of SST measurements from ships of opportunity, studies of SST variability in the Southern Hemisphere rely mainly on satellite-based data
sets. The leading EOF pattern of SST anomalies south of 20◦ S from the NOAA Optimum Interpolation SST data set for the period 1981–2005 from Ciasto & Thompson (2008) is reproduced in
Figure 9. This analysis was performed separately for the warm season, November–April, and thecold season, May–October. Superimposed on the SST EOF patterns are the associated 500 hPa
geopotential height anomaly patterns, obtained by regressing seasonal anomalies at each grid box
on the SST PC time series. Similar patterns are obtained for SLP (not shown). In both seasons,
EOF1 (Nov–Mar)

EOF1 (May–Oct)

Regressions on leading PCSST

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5.2. Extra Tropics

–0.8

–0.6

–0.4

–0.2

0.1

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0.5

0.7

Temperature (°C)

Figure 9
Sea surface temperature (SST) (shading) and 500-hPa geopotential height (contours) anomaly patterns associatedwith the leading principal component time series of monthly SST anomalies in the Southern Hemisphere(south of 20◦ S) during November–March (left) and May–October (right). The contours are drawn at ± 35 m,
25 m, 15 m, and 5 m, with negative values dashed. Adapted with permission from Ciasto & Thompson (2008).
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the leading EOF (in its positive polarity) exhibits positive SST anomalies across much of the South
Paciﬁc and negative SST anomalies in the Paciﬁc sector of the Southern Ocean. The warm-season
pattern also exhibits positive SST anomalies across the Indian and Atlantic sectors of the Southern
Ocean, with negative anomalies to the north and adjacent to the coast of Antarctica.
As Ciasto & Thompson (2008) demonstrated, the SST anomalies associated with the leading
mode of variability are largely atmospherically forced via changes in the surface turbulent ﬂuxes
and to a lesser extent Ekman currents. The associated 500 hPa geopotential height anomaly pattern
resembles that due to ENSO (recall Figure 6), with an additional component related to the SAM
during the warm season. Although the SST anomalies depicted in Figure 9 are primarily forcedby the atmosphere, there is evidence for a back effect onto the atmosphere, manifest as an increasein the persistence time for the SAM above that due to intrinsic atmospheric dynamical processes(Sen Gupta & England 2007).
5.2.2. Pacific Decadal Oscillation. The leading EOF of monthly SST anomalies over the North
Paciﬁc (after removing the global mean SST anomaly) and its associated PC time series are termed
the Paciﬁc Decadal Oscillation (PDO) after Mantua et al. (1997). This mode, which accounts for
25% of the variance of monthly anomalies in the HadISST data set during the period 1900–2008,
is displayed in Figure 10. Although the EOF calculation was restricted to the North Paciﬁc (20◦ –70◦ N), the pattern is displayed globally by regressing the monthly SST anomalies at each locationon the PC time series. The spatial pattern of SST anomalies associated with the PDO is similar tothat associated with ENSO (recall Figure 6) except for the relative weighting between the northand tropical Paciﬁc. In particular, the amplitudes of the SST anomalies in the equatorial eastern
Paciﬁc compared with those in the North Paciﬁc are comparable for the PDO; however, they are
considerably larger for ENSO (see also Zhang et al. 1997, Garreaud & Battisti 1999, Dettinger
et al. 2000, Deser et al. 2004). The PDO has also been termed the Interdecadal Paciﬁc Oscillation(Power et al. 1999) in recognition of its extension to the South Paciﬁc.
The time series of the PDO exhibits considerable decadal variability, hence its name (Figure 10,
bottom). Even the raw monthly anomalies predominantly show periods in which one sign lasts
for two decades or longer (for example, the periods 1908–1945, 1947–1976, and 1977–1998).
The decadal SST transitions are accompanied by widespread changes in the atmosphere, ocean,and marine ecosystems. For example, Mantua et al. (1997) found that the timing of changes in
the PDO closely corresponded to those in salmon catch along the west coast of North America.
The Aleutian low pressure system also ﬂuctuates in tandem with the PDO SST variations (Deseret al. 2004). Given the relatively short observational record, it is difﬁcult to ascertain whether
there is a robust spectral peak in the PDO time series. Some studies suggest nominal timescales of∼20 years and ∼50 years (Minobe 1997, Minobe 1999, Deser et al. 2004), whereas others conclude
that the PDO record does not differ signiﬁcantly from a ﬁrst-order autoregressive process (e.g.,
red noise: Pierce 2001, Qiu et al. 2007).
Many different mechanisms have been proposed for the PDO, including random forcingof the ocean by the atmosphere according to the stochastic climate model paradigm (recall
Section 2), the atmospheric bridge from the tropical Indo-Paciﬁc, extra-tropical ocean processes,
and atmosphere-ocean interactions within the North Paciﬁc (see the recent review in Alexander2009). How can we reconcile these different mechanisms for the PDO? Several studies haveused statistical analyses to reconstruct the PDO and determine the processes that underlie its
dynamics (Newman et al. 2003, Vimont 2005, Schneider & Cornuelle 2005, Qiu et al. 2007,
Newman 2007). Taken together, theses studies indicate that on interannual timescales, random
and ENSO-induced ﬂuctuations in the strength of the Aleutian low are almost equally importantin determining PDO variability via surface heat ﬂux forcing, with negligible contribution from
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–0.65

–0.55

25%

–0.45

–0.35

–0.25

–0.15

–0.05

0.05

0.15

0.25

0.35

0.45

0.55

0.65

Temperature (°C sd–1)

b PC time series
4Standard deviation

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a PDO

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2
0
–2
–4
1900

1920

1940

1960

1980

2000

Year
Figure 10
Paciﬁc Decadal Oscillation. (a) The leading empirical orthogonal function (EOF) of monthly sea surface
temperature (SST) anomalies over the North Paciﬁc (after removing the global mean SST anomaly) basedon the HadISST data set during 1900–2008. Although the EOF calculation was restricted to the North
Paciﬁc (region outlined by the black rectangle), the pattern is displayed globally by regressing the monthly SST
anomalies at each location on the principal component (PC) time series. (b) Associated PC time series
showing the unsmoothed record (red and blue bars) and the 5-year running mean record (black line).

ocean currents. On decadal timescales, stochastic heat ﬂux forcing, the atmospheric bridge, andchanges in the North Paciﬁc oceanic gyre circulation contribute approximately equally. Over thewestern Paciﬁc east of Japan where a deep mixed layer develops during winter, ocean circulation
changes associated with latitudinal excursions of the Kuroshio Current Extension are of primary
importance for PDO-related SST variability (Nonaka et al. 2006, Taguchi et al. 2007). A key
implication of these analyses is that, unlike ENSO, the PDO is likely not a single physical mode
but rather the sum of several phenomena.
The PDO is only one measure of SST variability in the North Paciﬁc. Other recurring patterns
include the North Paciﬁc mode (Deser & Blackmon 1995, Nakamura et al. 1997, Barlow et al.
2001, Guan & Nigam 2008), which is closely related to the PDO albeit with less amplitude in thetropical Indo-Paciﬁc. A distinct pan-Paciﬁc mode has also been identiﬁed, emphasizing variability
in the eastern portion of the North Paciﬁc (Guan & Nigam 2008). A North Paciﬁc Gyre Oscillationwww.annualreviews.org • Sea Surface Temperature Variability

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mode is also evident in sea surface height variability (Di Lorenzo et al. 2008). The mechanisms
governing these modes are not well understood.

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5.2.3. Atlantic Multidecadal Oscillation. SSTs in the North Atlantic undergo slow variations
with a period on the order of 65–80 years (Kushnir 1994, Schlesinger & Ramankutty 1994,
Delworth & Mann 2000, Enﬁeld et al. 2001, Ting et al. 2009, Guan & Nigam 2009). This phenomenon has been termed the Atlantic Multidecadal Oscillation (AMO) (Kerr 2000). A simple
index of the AMO is shown in Figure 11 for the period 1870–2008 based on the HadISST dataset (note that SST observations are relatively plentiful in the North Atlantic back to 1870; recall
Figure 3). This index is deﬁned as the area-average SST anomaly over the North Atlantic (0◦ –70◦ N) minus the global mean SST anomaly. Warm phases occurred from the late 1920s throughthe late 1960s and since the mid-1990s, and cool phases occurred from the early 1900s through

a AMO

–2.7

–2.1

–1.5

–0.9

–0.3

0.3

0.09

1.5

2.1

2.7

SST anomalies (°C)

b North Atlantic SST index
0.60

SST (°C)

0.30
0.00
–0.30
–0.60
1870

1890

1910

1930

1950

1970

1990

Year

Figure 11
Atlantic Multidecadal Oscillation. (a) Regression pattern of monthly sea surface temperature (SST)
anomalies (after removing the global mean SST anomaly) on the North Atlantic SST Index, based on the
HadISST data set during 1870–2008. (b) The North Atlantic SST Index, deﬁned as the average monthly SST
anomaly over the North Atlantic (0◦ –70◦ N) minus the global mean monthly SST anomaly (red and blue bars).
The green line depicts an estimate of the natural (e.g., not due to forcing external to the ocean-atmospheresystem) component of the 10-year low-pass-ﬁltered North Atlantic SST index from Ting et al. (2009).
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the mid-1920s and from the early 1970s through the mid-1990s. The spatial pattern associatedwith this time series, obtained by linearly regressing the SST anomalies at each location on the
AMO index, exhibits positive values over the entire North Atlantic, with the largest magnitudes(approximately 0.5◦ C) south of Greenland.
The AMO is associated with large-scale precipitation changes, most notably over the Sahel,
the southeastern United States, and Brazil (Enﬁeld et al. 2001, Ting et al. 2009), and with afrequency of severe Atlantic hurricanes (Enﬁeld et al. 2001). It should be noted that the AMO
is not signiﬁcantly correlated with the NAO (Guan & Nigam 2009). The precise nature of the
AMO is still being reﬁned and clariﬁed. In particular, the role of Paciﬁc SST variability is not wellunderstood. For example, Ting et al. (2009; see also Enﬁeld et al. 2001) showed some connection
between the AMO and SST anomalies in the Gulf of Alaska and the tropical Paciﬁc (see also
Figure 11), whereas the analysis of Guan & Nigam (2009) indicates that there are no signiﬁcant
SST linkages outside of the North Atlantic.
The AMO is considered to be a natural mode of oscillation of the Atlantic Ocean’s thermohaline
circulation (Delworth & Mann 2000). Modeling studies indicate that this mode is intrinsic tothe ocean and stochastically forced by atmospheric buoyancy ﬂuxes (Delworth & Greatbatch2000). However, the amplitude of the mode is augmented owing to coupled ocean-atmosphereinteractions.
Considerable debate concerns the degree to which anthropogenic effects may be contributing
to the recent positive phase of the AMO. In particular, there has been a general warming trend overboth land and ocean associated with increasing greenhouse gas concentrations. Because the AMO
index is a temperature-based record, it may contain the anthropogenic global warming signal andthus partially confound the true state of the AMO. There have been various attempts to remove theanthropogenic global warming signal from the AMO index, including simple linear detrending,removal of the global mean temperature anomaly (as in Figure 1), and removal of model-based
estimates of the forced component of variability. The latter approach provides the best estimateof the natural component of the AMO (Ting et al. 2009). This natural component (green curvein Figure 11) indicates that the AMO is currently in a modest warm phase, not an extreme warm
phase as would be inferred from the raw SST index (Ting et al. 2009, Guan & Nigam 2009).
However, the Atlantic thermohaline circulation may itself be altered under anthropogenic forcing
as projected by some climate models (Dixon et al. 1999, Wood et al. 1999), but this issue is distinct
from that of the overall global warming trend in the temperature-based AMO index.

SUMMARY POINTS
1. Interannual and longer timescale SST variations exhibit large-scale organization owing
to both atmospheric and oceanic processes.
2. Such large-scale organization may be due to intrinsic modes of atmospheric circulation
variability that imprint themselves upon the SST ﬁeld mainly via surface energy ﬂuxes
but also via Ekman currents. Examples include SST ﬂuctuations in the Southern Ocean
associated with the Southern Annular Mode, a tripolar pattern of SST anomalies in the
North Atlantic associated with the North Atlantic Oscillation, and a pan-Paciﬁc mode
known as the Paciﬁc Decadal Oscillation. Oceanic processes also contribute to the latter.
3. A leading paradigm for these atmospherically forced patterns of SST variability is thestochastic climate model whereby random ﬂuctuations in the atmospheric circulation
give rise to a low-frequency SST response.

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4. Patterns of nonseasonal SST variability may also result from coupled ocean-atmosphereinteractions, most prominently the El Niño-Southern Oscillation phenomenon in thetropical Indo-Paciﬁc sector, but also the tropical Atlantic Niño and the cross-equatorial
meridional modes of the tropical Paciﬁc and Atlantic.
5. Intrinsic oceanic modes also may give rise to large-scale patterns of SST variability. A
leading example is the Atlantic Multidecadal Oscillation associated with changes in theoceanic thermohaline circulation.

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FUTURE ISSUES
A number of outstanding issues remain regarding the patterns and mechanisms of nonseasonal SST variability; these are exempliﬁed in the following questions:
1. Does the response to global warming (e.g., increasing greenhouse gas concentrations)project onto known patterns of natural SST variability, or does it create new patterns?
2. How much does feedback from the ocean to the atmosphere inﬂuence the atmosphericallydriven modes of extratropical SST variability?
3. What is the nature of the predictability of the tropical and extratropical modes of SST
variability on decadal timescales?
4. What are the linkages among the different natural modes of SST variability, and whatmechanisms produce these connections?
5. Do high-resolution satellite data sets reveal new patterns of SST variability?
6. Do empirical methods such as EOF analysis identify the true physical modes of SST
variability?
7. How do the patterns of SST variability relate to those in the ocean interior?

DISCLOSURE STATEMENT
The authors are not aware of any biases that might be perceived as affecting the objectivity of thisreview.

ACKNOWLEDGMENTS
Support from the NOAA Climate Program Ofﬁce’s Climate Variability and Predictability Programis gratefully acknowledged. The National Center for Atmospheric Research is supported by the
National Science Foundation.

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Contents

Volume 2, 2010

Annu. Rev. Marine. Sci. 2010.2:115-143. Downloaded from arjournals.annualreviews.org
by NCAR on 12/15/09. For personal use only.

Paleophysical Oceanography with an Emphasis on Transport Rates
Peter Huybers and Carl Wunsch p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1
Advances in Estuarine Physics
Parker MacCready and W. Rockwell Geyer p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p35
The Effect of Submarine Groundwater Discharge on the Ocean
Willard S. Moore p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p59
Marine Ecomechanics
Mark W. Denny and Brian Gaylord p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p89
Sea Surface Temperature Variability: Patterns and Mechanisms
Clara Deser, Michael A. Alexander, Shang-Ping Xie, and Adam S. Phillips p p p p p p p p p p p p 115
Contemporary Sea Level Rise
Anny Cazenave and William Llovel p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 145
Estimation of Anthropogenic CO2 Inventories in the Ocean
Christopher L. Sabine and Toste Tanhua p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 175
Ocean Deoxygenation in a Warming World
Ralph F. Keeling, Arne Körtzinger, and Nicolas Gruber p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 199
Archaeology Meets Marine Ecology: The Antiquity of Maritime
Cultures and Human Impacts on Marine Fisheries and Ecosystems
Jon M. Erlandson and Torben C. Rick p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 231
The Ecology of Seamounts: Structure, Function, and Human Impacts
Malcolm R. Clark, Ashley A. Rowden, Thomas Schlacher, Alan Williams,
Mireille Consalvey, Karen I. Stocks, Alex D. Rogers, Timothy D. O’Hara,
Martin White,Timothy M. Shank, and Jason M. Hall-Spencer p p p p p p p p p p p p p p p p p p p p p p p 253
Microbial Provinces in the Subseaﬂoor
Matthew O. Schrenk, Julie A. Huber, and Katrina J. Edwards p p p p p p p p p p p p p p p p p p p p p p p p p p 279
Prochlorococcus: Advantages and Limits of MinimalismFrédéric Partensky and Laurence Garczarek p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 305
Oceanographic and Biogeochemical Insights from Diatom Genomes
Chris Bowler, Assaf Vardi, and Andrew E. Allen p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 333

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Genetic Perspectives on Marine Biological Invasions
Jonathan B. Geller, John A. Darling, and James T. Carlton p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 367
Biocomplexity in Mangrove Ecosystems
I.C. Feller, C.E. Lovelock, U. Berger, K.L. McKee, S.B. Joye, and M.C. Ball p p p p p p p p p p p 395
What Can Ecology Contribute to Ecosystem-Based Management?
Simon F. Thrush and Paul K. Dayton p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 419

Annu. Rev. Marine. Sci. 2010.2:115-143. Downloaded from arjournals.annualreviews.org
by NCAR on 12/15/09. For personal use only.

Bioluminescence in the Sea
Steven H.D. Haddock, Mark A. Moline, and James F. Case p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 443
ErrataAn online log of corrections to Annual Review of Marine Science articles may be found at
http://marine.annualreviews.org/errata.shtml

Contentsvii