Pacific Decadal Variability: Patterns and Processes

Pacific Decadal Variability: Patterns and processes

Shang-Ping Xie

International Pacific Research Center, University of Hawaii

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.

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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 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 the associated atmospheric variability--a forcing--is even less predictable.

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 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.

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 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.

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.

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