Pacific Decadal Variability: Patterns and processes
Shang-Ping Xie
International Pacific Research Center, University of Hawaii
xie@soest.hawaii.edu
Abstract. This is the note of a lecture delivered on February 5, 2001, at the Training Institute on Climate
and Society in the Asia-Pacific region, East-West Center, University of Hawaii. The note describes the
time-space patterns of Pacific decadal variability and its key processes, some of which have implications
for climate predictability.
_________________________
Pacific as evidence for positive ocean-atmosphere
interaction, without further analysis. Conversely, one
can also argue that the anomalous winds are the
one-way forcing for SST anomalies but know
nothing about the latter. It turns out that the latter
argument is truer in the central North Pacific where
PDO has a major center of action.
This scenario of one-way atmospheric forcing on
the ocean dates back to Hasselmann (1976). It is
based on a simple model where sea surface
temperature anomalies (SSTAs) are generated by
atmospheric heat flux forcing. Because of the large
heat content or thermal inertia of the ocean, the
spectrum of SSTs will be red with higher power at
low frequencies, in response to white noise forcing
with equal power at all frequencies. To the first
order, the observed SST spectrum is indeed red, and
the time-lag correlation analysis indicates that the
atmospheric variability leads the SST one by a
month, in support of the stochastic forcing theory.
Frankignoul (1985) is a good review of this subject.
Given a large portion of observed SST variability
is forced by stochastic atmospheric forcing, why are
there preferred geographic locations where SST
variability is particularly large? The central North
Pacific between 25-40N is one of such regions. This
geographic distribution of SST variability has to do
with the atmospheric dynamics. In winter, the
westerly jet shows strong zonal variations that lead
to the formation of geographically stationary modes
of variability. The Pacific North-America (PNA)
pattern and North Atlantic Oscillation (NAO) are the
two dominant modes of the atmospheric variability
in the Northern Hemisphere. In the Pacific, strongest
surface wind variability associated with the PNA is
located in the central North Pacific between 20-40N,
roughly coinciding with PDO’s center of action. In
short, the atmospheric stochastic forcing may be
white in time, but has well-organized spatial
structure such as PNA. To the extent it is largely a
response to atmospheric (PNA) forcing, central
Pacific SST variability is not very predictable and
1. Introduction
Pacific decadal oscillation (PDO), Pacific decadal
variability (PDV) and interdecadal Pacific
oscillation (IPO) all denote the same climate
variability observed in the Pacific Ocean on time
scales from one or a few decades. The diverse
naming itself indicates a lack of good understanding
of this phenomenon (ENSO became widely used
because it captures the essence of the phenomenon:
ocean-atmosphere interaction). In the equatorial
Pacific, SST variability is dominated by interannual
variability or ENSO while in the mid-latitude North
Pacific; it contains more power at lower frequencies
with decadal or longer time scales (Deser and
Blackmon 1995). A recent major phase shift of the
PDO took place in the mid-1970s, which has been a
focus of many recent investigations (Nitta and
Yamada 1989; Deser et al. 1996; Yasuda and
Hanawa 1997).
The PDO index is a popular index that is defined
as the principal component of the first empirical
orthogonal function (EOF) of North Pacific SST
variability north of 20N (Mantua et al. 1997). The
influence of this PDO or PDV is prevalent and
related to variability of winter surface air
temperature and precipitation over North America
(Minobe 1997; Mantua et al. 1997), of summer
rainfall in China (Huang et al. 1999), of climatic
variables in Australia (Power et al. 1999). It is now
clear that decadal variability needs to be taken into
consideration in making climate prediction to
improve the prediction itself if PDV is predictable.
Even if PDV is not predictable, it still needed to be
accounted for as an important contributor to the
uncertainties/errors of the prediction.
2. Atmospheric forcing
The rest of the lecture will focus on the key
physical processes involved in the PDV. Encouraged
by the success in understanding the ENSO, one
might conclude from the collocation of SST cooling
and intensified westerly winds in the central North
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the associated atmospheric variability--a forcing--is
even less predictable.
impact on SST (Qiu 2000; Tomita et al. 2001).
Under observed wind stress forcing, OGCM is
capable of reproducing the observed temperature
variability not only in the subsurface but at the
surface as well. (The simulation of the subsurface
variability tends to be better than that of SST,
though.) This, plus the fact that KOE SSTAs tend to
lag behind the SSTAs in the central North Pacific by
a few years (Nakamura et al. 1997; Miller et al.
2000) presumably because of the westward-traveling
Rossby waves, suggests that the KOE temperature
variability is potentially predictable. Whether the
KOE SSTAs can significantly affect the atmosphere
is an open question under active investigation [some
atmospheric GCM suggests that the atmosphere may
be particularly sensitive to the KOE SSTAs (Peng et
al. 1997)]. Nevertheless, this predictability of ocean
temperature can still be exploited for fishery
resources prediction. Climate for fish is more
predictable than the climate for humans.
3. Key oceanic processes
Then, is there anywhere in the ocean where the
slow subsurface dynamics of the ocean--like Rossby
waves or advection--can contribute significantly to
SST variability. Recently, a systematic search for
such regions has been done by driving an ocean
general circulation model (OGCM) with observed
wind stress but restoring SST and surface salinity
back to observed monthly climatology. The latter
surface buoyancy conditions remove anomalous
atmospheric thermal forcing and thus allow a close
look into the role of ocean dynamics. In this OGCM,
the largest decadal SST variability is found in the
equatorial Pacific, as expected, and in the
Kuroshio-Oyashio Extension 30-45N (Xie et al.
2000). These two regions offer strong
surface-subsurface connection (SSC) where
subsurface temperature variations can impact
strongly on SST.
The equatorial SSC is provided by oceanic
upwelling that keeps the eastern equatorial Pacific
cold. Even on the equator, the ocean decreases with
depth below the mixed layer. The subsurface ocean
is kept cold by a subtropical circulation (STC) that
transports cold mid-latitude surface water along the
thermocline into the equator, where this cold
upwells to the surface (McCreary and Lu 1994; Liu
et al. 1994). Two mechanisms can conceivably
affect subsurface ocean temperature on the equator,
which can in turn affect SST and the atmosphere.
One is through STC’s advection of SST anomalies
in the mid-latitudes (Gu and Philander 1997), and
one is through changes in STC strength and hence
its transport of cold water (Kleeman et al. 1999; A.
Solomon and J.P. McCreary 2000, pers. comm.).
Recent OGCM simulations suggest that the former
mechanism is not very efficient in preserving the
subducted mid-latitude SSTA (Schneider et al. 1999;
Nonaka and Xie 2000). When the advected
temperature anomalies arrive at the equator, they
decay to less than 10% of their original size in the
mid-latitudes. On the other hand, OGCM
simulations at IPRC show that equatorial SST
anomalies co-vary with the STC transport on
decadal time scales (Nonaka et al. 2000), in sharp
contrast to interannual variability but in line with the
Kleeman et al. mechanism.
The KOE transports huge amount of heat that is
released to the atmosphere. The KOE SSC is due to
the deep ocean mixed layer in winter and early
spring, which allows subsurface anomalies
generated by anomalous geostrophic advection to
4. Other related issues
In addition to PDO that tend to center on the
mid-latitude North Pacific, the Arctic Oscillation
(AO) or the hemispheric manifestation of the NAO
affects strongly the high-latitude northern continents
and oceans (Thompson and Wallace 1998; Xie et al.
1999). Winter air temperature variability in northern
Japan, northern China and Siberia is highly
correlated with AO/NAO on a quasi-decadal time
scale (Xie et al. 1999).
How
predictable
is
the
atmospheric
variability--with which we humans are most
concerned--if we could predict SSTAs perfectly
everywhere on the globe? We address this question
by analyzing a 20-member ensemble simulation by
an AGCM forced with observed SST and sea ice for
the last 40 years. These 20 model integrations differ
only in their initial conditions. We hope that the
average among these 20 members will tell us about
the part of the atmospheric variability that is forced
by prescribed SST variations, while the
intra-ensemble spread will give a measure of the
internal chaotic variability that is not predictable. In
the Northern Hemisphere, the PNA response to
ENSO is the most predictable, followed by the NAO
in boreal spring (Huang et al. 2001). However, the
intra-ensemble variance is more similar to the
observed variance than the variance of the ensemble
mean, again in support of the Hasselmann
hypothesis. A caveat here is that this interpretation is
valid only if AGCMs are a good representation of
the real atmosphere, an assumption that is being
questioned and under investigation.
The global-mean surface air temperature has risen
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by 0.4C since 1975 after a relative stable period of
1940-1975. The regional distribution of this global
warming shows rich structures, with largest warming
over northern continents in the Northern Hemisphere
and cooling over the North Pacific and Atlantic. The
PNA and NAO again stand out in the sea level
pressure anomaly map. In fact, much of the recent
Northern Hemisphere warming can be accounted for
by the phase-shifting events of the PDO and NAO in
the 1970s. It suggests that the global warming, most
likely forced by the increasing green house gases in
the atmosphere, projects strongly onto the modes of
natural climate variability (Palmer 1998), giving
another good reason for studying PDO.
Huang, G., S.-P.Xie and S. Matsumura, 2001: A
20-member ensemble simulation using an
atmospheric GCM. J. Meteor. Soc. Japan, in
preparation.
Huang, R.H. et al., 1999: The interdecadal variation
of summer precipitation in China and the drought
trend in North China. Plateau Meteorology, 18,
465-476.
Kleeman, R. J.P. McCreary and B.A. Klinger, 1999:
A mechanism for generating ENSO decadal
variability. Geophys. Res. Lett., 26, 1743-1746.
Kumar, K.K., B. Rajagopalan and M.A. Cane, 1999:
On the weakening relationship between the
Indian monsoon and ENSO. Science, 284,
2156-2159.
Latif, M. and T.P. Barnett, 1994: Causes of decadal
climate variability over the North Pacific and
North America. Science, 266, 634-637.
Liu, Z., S.G.H. Philander and R.C. Pacanowski,
1994: A GCM study of the tropical-subtropical
upper-ocean water exchange. J. Phys. Oceanogr.,
24, 2606-2623.
Mantua, N.J., S.R. Hare, Y. Zhang, J.M. Wallace
and R.C. Francis, 1997: A Pacific interdecadal
climate oscillation with impacts on salmon
production. Bull. Amer. Meteor. Soc., 78,
1069-1079.
McCreary, J.M. and P. Lu, 1994: Interaction
between the subtropical and equatorial ocean
circulation. J. Phys. Oceanogr., 24, 466-497.
Miller, A.J. and N. Schneider, 2000: Interdecadal
climate regime dynamics in the North Pacific
Ocean: Theories, observations and ecosystem
impacts. Progress in Oceanography, 27, 257-260.
Minobe, S., 1997: A 50-70-year climatic oscillation
over the North Pacific and North America.
Geophys. Res. Lett., 24, 683-686.
Nakamura, H., G. Lin and T. Yamagata, 1997:
Decadal climate variability in the North Pacific
during recent decades. Bull. Amer. Meteor. Soc.,
78, 2115-2225.
Nitta, T. and S. Yamada, 1989: Recent warming of
tropical sea surface temperature and its
relationship to the Northern Hemisphere
circulation. J. Meteor. Soc. Japan, 67, 375-382.
Nonaka, M. and S.-P. Xie, 2000: Propagation of
North Pacific interdecadal subsurface temperature
anomalies in an ocean GCM. Geophys. Res. Lett.,
27, 3747-3750.
Nonaka, M., S.-P. Xie, and J.P. McCreary, 2000:
Decadal variations of the strength of the Pacific
subtropical cells and their effect on the tropical
heat balance. Proc. of Fall Meeting of Jap.
Oceanogr. Soc.
Palmer, T.N., 1998: Nonlinear Dynamics and
5. Summary
 Pacific Decadal Oscillation (PDO), Arctic
Oscillation (AO) and other climatic modes, in
addition to ENO, need to be taken into
consideration in making climate prediction;
 A large portion of extratropical SST variability
is forced by geographically stationary modes
(PNA & NAO) of atmospheric chaos and hence
unpredictable;
 The equatorial Pacific (EP) and the Kuroshio
and Oyashio Extension (KOE) are regions
where slow ocean dynamics impacts SST
variability (SST and marine ecological
conditions may be potentially predictable);
 Key ocean processes: adjustment of the
subtropical cell strength for EP & Rossby waves
for KOE;
 The global warming may manifest itself in
natural atmospheric/climatic modes (PNA &
NAO), particularly on regional scales.
REFERENCES
Deser, C. and M.L. Blackmon, 1995: On the
relationship between tropical and North Pacific
sea surface temperature variations. J. Climate, 8,
1677-1680.
___, C., M.A. Alexander and M.S. Timlin, 1996:
Upper-ocean thermal variations in the North
Pacific during 1970-1991. J. Climate, 9,
1840-1855.
Frankignoul, C., 1985: Sea surface temperature
anomalies, planetary waves, and air-sea feedback
in the middle latitudes. Rev. Geophys., 23,
357-390.
Gu, D. and S.G.H. Philander, 1997: Internal climate
fluctuations that depend on exchanges between
the tropics and extratropics. Science, 275,
805-807.
Hasselmann, K., 1976: Stochastic climate models. I:
Theory. Tellus, 28, 473-485.
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Climate Change: Rossby's Legacy. Bull. Amer.
Meteor. Soc., 79, 1411–1424.
Peng, S., W. A. Robinson, M. P. Hoerling, 1997:
The Modeled Atmospheric Response to
Midlatitude SST Anomalies and Its Dependence
on Background Circulation States. J. Climate, 10,
971–987.
Power, S., T. Casey, C. Folland, A. Colman and V.
Mehta, 1999: Inter-decadal modulation of the
impact of ENSO on Australia. Clim. Dynamics,
15, 319-324.
Qiu, B., 2000: Interannual variability of the
Kuroshio extension system and its impact on the
wintertime SST field. J. Phys. Oceanogr., 30,
1486–1502.
Schneider, N.S., S. Venzke, A.J. Miller, D.W.
Pierce, T.P. Barnett, C. Deser and M. Latif, 1999:
Pacific thermocline bridge revisited. Geophys.
Res. Lett., 26, 1329-1332.
Thompson, D. W. J, and J. M. Wallace, 1998: The
Arctic Oscillation signature in the wintertime
geopotential height and temperature fields.
Geophys. Res. Lett., 25, 1297–1300.
Tomita, T., S.-P. Xie and M. Nonaka, 2001: Decadal
Surface and Subsurface Variability in the
Kuroshio-Oyashio Extension: GCM Simulation
and Observations, J.Climate, submitted.
Xie, S.-P., T. Kunitani, A. Kubokawa, M. Nonaka,
and S. Hosoda, 2000: Interdecadal thermocline
variability in the North Pacific for 1958–97: A
GCM simulation. J. Phys. Oceanogr., 30,
2798-2813.
____, H. Noguchi, and S. Matsumura, 1999: A
hemispheric-scale quasi-decadal oscillation and
its signature in Northern Japan. J. Meteor. Soc.
Japan, 77, 573–582.
Yasuda, T. and K. Hanawa, 1997: Decadal changes
in the mode waters in the midlatitude North
Pacific. J. Phys. Oceanogr., 27, 858-870.
Useful website
http://tao.atmos.washington.edu/data_sets/#time_series
This site provides many useful climate indices such as Southern Oscillation Index (SOI), PDO, NAO and
AO.
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