Fine Pattern of Natural Modes in Sea Surface ... - AMS Journals

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JOURNAL OF PHYSICAL OCEANOGRAPHY

VOLUME 38

Fine Pattern of Natural Modes in Sea Surface Temperature Variability: 1985–2003 GE CHEN

AND

HAITAO LI

Key Laboratory of Ocean Remote Sensing, Ministry of Education, Ocean Remote Sensing Institute, Ocean University of China, Qingdao, China (Manuscript received 17 April 2006, in final form 29 May 2007) ABSTRACT A natural mode refers, in this study, to a periodic oscillation of sea surface temperature (SST) that is geophysically significant on a global, regional, or local scale. Using a newly developed harmonic extraction scheme by Chen, which has the advantage of being space–time decoupled and fully data adaptive, a variety of natural modes have been recovered from global monthly SST data for the period of 1985–2003. Among them, the eight most significant modes are identified as primary modes, whose spatial patterns are presented, along with their phase distributions. At seasonal time scales, a 4-month primary mode is uncovered in addition to the well-documented annual and semiannual cycles. At interannual time scales, the dominant El Niño–Southern Oscillation (ENSO) variability is found to be composed of at least five primary modes, with well-defined central periods around 18, 25, 32, 43, and 63 months. At time scales beyond ENSO, a decadal SST signal with an average period of 10.3 yr is observed. A unique contribution of this study is the derivation and presentation of fine patterns of natural SST modes and signals in joint dimensions of time, space, period, and phase, leading to several findings and conclusions that are of potential importance: 1) The degree of separability and regularity of the sub-ENSO modes is surprising, and thus reveals new details on the nature of this event. 2) The midlatitude counterparts of the equatorial interannual and decadal SST modes/signals are found in the two hemispheres with a frequency shift toward longer periods. The “shadows” of the Pacific Ocean’s ENSO modes are also observed with some detail in the Atlantic and the Indian Oceans. All of these provide direct evidence that teleconnections exist between the equatorial and extratropical oceans, as well as among the tropical Pacific, tropical Atlantic, and tropical Indian Oceans, possibly as a result of the “atmospheric bridge.” 3) A sharply opposite anisotropy is observed in the spatiotemporal pattern between the interannual modes and decadal signals, implying that they are potentially of a categorical difference in origin. 4) Locality or regionality is a fundamental feature for most of the SST modes. Treating the interannual or decadal variability as a single ENSO or Pacific decadal oscillation mode appears to be an oversimplification, and may lead to inappropriate interpretations. The results herein represent an improved knowledge of the natural variability in sea surface temperature, which will hopefully help to enhance the understanding of natural fluctuations of the global/regional climate system in the context of ocean–atmosphere interaction.

1. Introduction Sea surface temperature (SST) is one of the fundamental variables that are most widely used to describe the coupled ocean–atmosphere system. SST is, on one hand, a surface indicator or manifestation of many geophysical and biological processes in the ocean, such as ocean currents, planetary waves, ocean primary productivity, and marine fisheries. On the other hand, it

Corresponding author address: Ge Chen, Ocean Remote Sensing Institute, Ocean University of China, 5 Yushan Road, Qingdao 266003, China. E-mail: [email protected] DOI: 10.1175/2007JPO3592.1 © 2008 American Meteorological Society

JPO3592

constitutes the bottom boundary condition of the atmosphere and plays a key role in the air–sea exchanges of material, momentum, and energy. Furthermore, the importance of SST also lies in its strong influence on climate variability and predictability, which may affect many aspects of nature and society. Many dominant oceanographic/climatic events or processes, such as the El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the North Atlantic Oscillation (NAO), are known to have an SST dimension (acronyms not expanded in the text are defined in Table 1). Further understanding of these important events relies, to a large extent, on a better knowledge of the characteristics and dynamics of

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CHEN AND LI TABLE 1. List of acronyms.

Acronym

Expansion

COADS COARE

Comprehensive Ocean–Atmosphere Dataset Coupled Ocean–Atmosphere Response Experiment Climatic Research Unit European Centre for Medium-Range Weather Forecasts Empirical orthogonal function U.S. Naval Fleet Numerical Oceanography Center Global Precipitation Climatology Project Hadley Centre Ice and Sea Surface Temperature Intertropical convergence zone Japan Meteorological Agency Monsoon Experiment (Multichannel) Singular spectrum analysis North Atlantic National Aeronautics and Space Administration National Center for Atmospheric Research National Centers for Environmental Prediction National Environmental Satellite Data and Information Service National Oceanic and Atmospheric Administration National Oceanographic Data Center North Pacific Orthogonal wavelet transform Principal component analysis South Atlantic Southwest Fisheries Center Singular value decomposition Tropical Pacific Met Office

CRU ECMWF EOF FNOC GPCP HadISST ITCZ JMA MONEX (M)SSA NA NASA NCAR NCEP NESDIS NOAA NODC NP OWT PCA SA SFC SVD TP UKMO

SST variability at seasonal-to-decadal time scales. During the past 30 years or so, significant progress has been made in characterizing low-frequency variations of SST, especially in identifying its principal modes at intraseasonal-to-multidecadal periods, as a result of the availability of several reconstructed century-long SST datasets (e.g., Kaplan et al. 1998; Smith and Reynolds 2004; Rayner et al. 2006). Below is a brief review of some research that has been done on this topic (Table 2). Starting from the events with low periods below 2 yr, there are well-known annual and semiannual cycles as well as intraseasonal oscillations for SST. As early as over 30 years ago, Weare et al. (1976) noted that the seasonal variation of Pacific Ocean SST is dominated by a mode with a 12-month periodicity and greatest coherence in higher latitudes. They also showed a second important seasonal mode with a period of approximately 6 months and a large amplitude in the North Pacific. Levitus (1987) presented the first global estimates of the annual and semiannual SST harmonics in

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both amplitude and phase distributions. He found that maxima of the annual mode are located off Japan and the east coast of the United States and Canada. In the Southern Hemisphere, open-ocean maxima are located at latitudes 28°–32°S in the Pacific, Atlantic, and Indian Oceans. Maximum amplitudes of the semiannual mode occur in the North Pacific, in the North Atlantic off Newfoundland, and in the Arabian Sea off the coast of Somalia. A significant intraseasonal SST mode with a dominant period of 30–50 days was also observed in the tropical oceans (e.g., Krishnamurti et al. 1988). Recent studies revealed that a fast near-annual mode of coupled variability with a period of around 12–18 months exists in the tropical Pacific ocean–atmosphere system, as evidenced in SST, sea level, and surface wind anomalies (e.g., Jin et al. 2003). This mode, termed the Pacific Ocean basin mode, appears to be independent of the slow ENSO mode of SST variability (Kang et al. 2004). An ENSO-related SST mode in the Pacific Ocean was first discovered by Weare et al. (1976) and later confirmed by many others. Hsiung and Newell (1983) suggested that this mode varies in phase in the entire tropical oceans, and Kawamura (1994) pointed out that it has a quasi-periodicity of 2–5 yr. Meanwhile, Rasmusson et al. (1990) identified two dominant time scales of ENSO-related SST variability: one is a biennial mode with periods near 24 months, and the other is a lower-frequency concentration of variance in periods of 4–5 yr. Using 100 yr of global land, air, and sea surface anomaly data, Mann and Park (1994) reported significant peaks within a broad period band ranging from 2.8 to 5.7 yr, which exhibit characteristic ENSO patterns. They further divided this variability into higher- (2.8–3.0 and 3.3–3.4 yr) and lower-frequency (4.3–4.8 and 5.1–5.7 yr) bands. In addition, a quasibiennial mode centered near a 2.2-yr period and a mode centered at a 7–8-yr period were also observed, both exhibiting a predominantly NAO-related temperature pattern. In a study by Jiang et al. (1995), it was found that the dominant peak of the SST spectrum within the ENSO band is centered at about 52 months; this is referred to as the quasi-quadrennial mode. They also found that quasi-biennial variability is split between two modes, with periods near 28 and 24 months. In the past decade, Zhang et al. (1998) detected two interannual SST oscillations with periods of 51 and 26 months. The former exhibits propagation of SST anomalies northeastward from the Philippine Sea and then eastward along 40°N, but behaves more like a standing wave over the tropical Pacific. The latter is localized in the tropics and is characterized by the westward propagation of SST anomalies near the equator.

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TABLE 2. A partial summary of previously identified principal modes in SST variability. Acronyms are defined in the text or in Table 1. Identified principal modes Reference

Data source

Method

Weare et al. (1976)

NCAR (1949–62) SFC, JMA (1963–73)

EOF

Hsiung and Newell (1983) Levitus (1987)

FNOC (1949–79)

SVD

NODC, COADS (1854–1979)

Krishnamurti et al. (1988)

JMA (1979–85), MONEX (1979), ECMWF (1979) Ship and station data (1950–87)

Rasmusson et al. (1990)

Regime

Frequency

Location

1 1 2 2

6 months 12 months Interannual Interannual

NP Higher latitudes TP Entire tropical oceans

3

Interdecadal

Pacific, Atlantic

Harmonic analysis

1 1

6 months 12 months

Time series analysis

1

30–50 days

NP, NA, Arabian Sea Off east Japan, U.S., Canada, and 28°-32°S Western Pacific, Bay of Bengal

Space–time analysis

2

Near 24 months

2 2 3 2

4–5 yr 2–5 yr Interdecadal 2.2, 2.8–3.0, 3.3–3.4, 4.3–4.8, 5.1–5.7, 7–8 yr

3 1 2 2 2 3 2 3 2 3 3 3 3 3 3 3 3 3

15–18 yr 15 months 24, 28, 52 months 24–30, 45, 60–65 months 7–8 yr 13–15 yr 26, 51 months Interdecadal 43.7 months Interdecadal Multidecadal Multidecadal Multidecadal Interdecadal Interdecadal Interdecadal Multidecadal Interannual to interdecadal 13.9 yr 14.4 yr 42 yr 12–18 months 12–18 months

NA NA NA TP TP

Kawamura (1994) Mann and Park (1994)

UKMO (1955–88)

Rotated EOF

CRU (1891–1990)

SVD

Jiang et al. (1995) Moron et al. (1998)

COADS (1950–90)

MSSA

UKMO (1901–94)

MSSA

Equatorial eastern Indian Ocean TP NP, Indian Ocean NP, NA, North America, western Europe, central Asia Tropical and extratropical Equatorial Pacific Equatorial Pacific Eastern TP NA NA TP NP, TP Pacific Pacific NP High latitudes of NA NA Eastern NP Eastern TP Central TP NP SA

Zhang et al. COADS (1950–93) (1998) Enfield and UKMO (1956–91) Mestas-Nuñez (1999)

MSSA

Mestas-Nuñez and Enfield (1999)

UKMO (1956–91)

Rotated EOF

Mizoguchi et al. (1999)

COADS (1947–92)

Complex EOF

Jin et al. (2003) Kang et al. (2004) Frauenfeld et al. (2005) Lohmann and Latif (2005)

NCEP (1959–2002) NCEP (1980–2001)

Space–time analysis Space–time analysis

3 3 3 1 1

UKMO (1949–2000)

PCA

3

Interdecadal

TP

HadISST (1870–1998)

Space–time analysis, model simulation

2 3

About 4 yr About 10 yr

Central western Pacific Central western Pacific

Complex EOF

Moron et al. (1998) presented three interannual signals with periods of about 24–30, 45, and 60–65 months in the tropical eastern Pacific. They also identified another 7–8-yr oscillation, which involves the entire double-gyre circulation of the North Atlantic. Enfield

and Mestas-Nuñez (1999) summarized the classic features normally associated with the Pacific ENSO mode as follows: (i) a region of intense amplitude within ⫾10° of the equator and east of the date line, which decreases to small amplitude over a wide, wedge-shaped region

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that spreads poleward along the eastern boundary; (ii) phase propagation northward along the coast of North America, with maximum lags of one to two seasons in the Gulf of Alaska and the Bering Sea; and (iii) regions of intermediate amplitudes and opposite phases in the central North Pacific and the central South Pacific. They also specified the nominal periodicity of this mode was 43.7 months. An ENSO mode with a dominant period of about 4 yr in the central western Pacific was recently diagnosed by Lohmann and Latif (2005). Next to the seasonal cycle and ENSO, the third largest signal to emerge in SST variability is an interdecadal oscillation. Hsiung and Newell (1983) reported evidence of downward SST trends in the North Atlantic and North Pacific in the 1965–79 period, accompanied by upward trends in the South Atlantic and South Pacific. Kawamura (1994) showed that this mode is characterized by increasing Indian Ocean SST and decreasing central North Pacific SST on an interdecadal time scale. Moreover, he found two dominant interdecadal modes, which are confined to specific regions of the Atlantic Ocean. Mann and Park (1994) showed an interdecadal mode with a period of 15–18 yr, which appears to represent long-term ENSO variability. They also found a potentially significant decadal mode centered on an 11–12-yr period, which exhibits an NAOrelated temperature pattern. Zhang et al. (1998) observed an interdecadal standing SST mode with opposite signs in the North and tropical Pacific. Moron et al. (1998) identified that near-decadal oscillations exist primarily over the North Atlantic, but they also exist over the South Atlantic and the Indian Ocean. Their results indicated a 13–15-yr oscillation with a seesaw pattern between the Gulf Stream region and the North Atlantic drift. In a work by Enfield and Mestas-Nuñez (1999), three non-ENSO low-frequency modes were characterized. The first mode corresponds to the Pacific interdecadal variability whose spatial pattern is similar to that of PDO. The second mode corresponds to the Pacific multidecadal variability, with the highest loadings extending westward from 160°W along 45°N. The third mode corresponds to the Atlantic multidecadal variability, which is most evident in the high latitudes of the North Atlantic, immediately south of Greenland. In an accompanying study, Mestas-Nuñez and Enfield (1999) derived six localized non-ENSO modes: The North Atlantic multidecadal mode, the eastern North Pacific interdecadal mode, the eastern tropical Pacific interdecadal mode, the central tropical Pacific interdecadal mode, the North Pacific multidecadal mode, and the South Atlantic interannual-to-interdecadal mode. They concluded that non-ENSO SST variability is more re-

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gional than global in nature. This view was shared by Mizoguchi et al. (1999) who also found three multi- and quasi-decadal variations of SST in the North Atlantic. They described their mode 1 as a very slow oscillation with an approximate 42-yr period, having basinwide spatial evolution. Their modes 2 and 3 are a quasidecadal fluctuation with periods of 14.4 and 13.9 yr, propagating from the Labrador Sea eastward, following the North Atlantic Current and the subpolar gyre. There are some disagreements on the validity of the identified interdecadal modes associated with SST variability. Zhang et al. (1997) argued that it is not possible to separate decade-to-century-scale variability from interannual variability using straightforward principal component analysis (PCA) and related techniques. In contrast, Frauenfeld et al. (2005) reported the discovery of a distinctly interdecadal signal in the climate of the Pacific Ocean, which they found by examining the coupled behavior of SST and Northern Hemisphere atmospheric circulation and called the interdecadal Pacific signal. Lohmann and Latif (2005) also observed a decadal mode with a period of about 10 yr in the SST of the central western Pacific. These inconsistent arguments suggest that the cause and nature of interdecadal SST variability is still, in a large part, unclear. To summarize, the identified modes of SST variability at time scales from seasonal to multidecadal can be grouped into three regimes: the annual (1–18 months), interannual (1.5–8 yr), and decadal (8–100 yr) regimes. Within each regime, however, the specific modes identified are widely scattered in terms of the number of modes, their peak frequencies, and their spatial patterns (see Table 2 for a partial summary). Such a result is not surprising, given the fact that neither the datasets employed nor the methodologies applied are exactly the same for any two of the published studies. Datasets are known to have different sources, durations, resolutions, and quality (e.g., Kaplan et al. 1998; Smith and Reynolds 2004; Rayner et al. 2006). As far as the extraction schemes are concerned, a variety of methods have been used, including EOF (e.g., Weare et al. 1976; Hsiung and Newell 1983; Kawamura 1994; Enfield and Mestas-Nuñez 1999), SVD or PCA (e.g., Mann and Park 1994), SSA (e.g., Jiang et al. 1995; Moron et al. 1998), OWT (e.g., Mak 1995), or their variations. Each of these methods has its well-known strengths and sometimes unknown weaknesses (see Moron et al. 1998 for a review), and therefore the methods must be used with caution. It is the authors’ view that a large portion of the inconsistency in published SST modes may result from nonoptimal use of the statistical methods involved. Ultimately, a natural mode is a point-specific rather than an area-specific property as

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far as SST is concerned; that is, none of the two different sites, however close they are, can have exactly the same suite of natural modes. This determines that the performance of any statistical method, which relies on the assumption that a spatiotemporal coherence exists in the variation of a signal, will be degraded. Based on such understanding, a three-dimensional (3D) harmonic extraction scheme has been designed to identify natural modes in space–time geophysical variability (Chen 2006) as an alternative to existing conventional or modern methodologies. In the present study, the proposed scheme is applied to a 221-month satellite-based SST dataset with anticipation that fine patterns of natural modes in SST variability will be revealed with unprecedented resolution in both the space and frequency domains. Following this introduction, the SST data used in our analysis are described, along with a brief description of the proposed harmonic extraction scheme in section 2. The identified natural modes of global and regional SSTs are presented in section 3. The spatiotemporal characteristics of natural SST modes with detailed patterns are revealed and discussed in section 4. Finally, a summary with concluding remarks is given in section 5.

2. Data and method The SST data used in this study were extracted from a NOAA–NASA Oceans Pathfinder product called the “Equal Angle Best SST” for the period from January 1985 through May 2003. This dataset has a spatial resolution of 9 km and a temporal resolution of 1 month (Vazquez et al. 1998). In the equal-angle projection there is an equal number of pixels in both the longitude and latitude directions. The 9-km dataset consists of data with 4096 pixels in the east–west direction (longitudinal) and 2048 pixels in the north–south direction (latitudinal). This product retains, after a series of statistical tests, only high-quality pixels. All versions of the Pathfinder SST algorithm are based on the NOAA/ NESDIS nonlinear SST operational algorithm (Kilpatrick et al. 2001). An optimal interpolation was performed to reconstruct the SST dataset with a 1° ⫻ 1° spatial resolution on a monthly basis, yielding a time series of 221 values for each grid point. Note that satellite data were rarely used for SST mode identification in previous research (see Table 2), mainly because they are of relatively short duration. However, it is believed that their unique advantages over reconstructed historical datasets—high accuracy and spatial resolution, as well as unprecedented geographical homogeneity— make them extremely attractive for exploring modal patterns with fine spatial and temporal resolution im-

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bedded in global SST variability. The availability of a continuous dataset nearly two decades long allows for a representative recovery of seasonal-to-interannual variability; meanwhile, it allows a preliminary identification of decadal signals. The main technique that will be used for mode identification in this analysis is a newly developed 3D harmonic extraction scheme, a detailed description of which can be found in Chen (2006). The scheme performs a moving harmonic analysis with respect to longitude, latitude, and extracting period, resulting in a full recovery of the amplitude and phase as a function of space and period within the resolvable band. Given its “searching” nature, no prior knowledge or assumption on either data or mode property is needed; or, in other words, it is considered to be fully data adaptive. The effectiveness and advantage of this methodology have been clearly demonstrated in an earlier study using global precipitation data (Chen 2006).

3. Identification of natural modes in global and regional SSTs We start by recalling the classic SST climatology (Fig. 1a). The global SST distribution is dominated by a zonal structure with well-known features, such as the warm pool in the western tropical Pacific and the South Equatorial Current in the eastern tropical Pacific. The gradual decrease of SST from equatorial to polar regions is basically controlled by solar heating. In contrast, the spatiotemporal variability of SST (Fig. 1b) exhibits much irregularity compared to its climatology. Figure 1b was obtained by averaging the 221 extracted harmonic amplitudes at each grid point, and it thus represents the geographical pattern of the overall dynamic level of SST variations in the frequency domain (note that it could be considerably different from the monthly variance of SST at each grid point). This pattern either can be interpreted as a measure of the richness of SST modality in the world’s oceans, or, in a sense, can be called an SST “modal index.” Primary maxima are found in Niño-1 ⫹ -2 and Niño-4 regions, as well as in a 15°-wide zonal band along 40°N in the North Pacific, and a southwest–northeast-oriented area off the east coasts of the United States and Canada in the North Atlantic. These are the places where multimodality is most likely to occur. They cover almost all of the core regions where a dozen ENSO-, PDO-, and NAO-related SST modes have been identified (see Table 2). The observed high SST variability at midlatitudes is mainly related to the combination of Ekman transport and changes in sensible and latent heat fluxes as a result of strong variations in surface wind stress in

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FIG. 1. (a) Global SST climatology derived from Pathfinder data spanning January 1985 through May 2003; (b) global SST variability obtained by averaging the 221 extracted harmonic amplitudes [the three white boxes indicate (from right to left) the regions of Niño-1 ⫹ -2 (0°–10°S, 270°–280°E), Niño-3 (5°S– 5°N, 210°–270°E), and Niño-4 (5°S–5°N, 160°–210°E), respectively]; (c) global precipitation climatology derived from GPCP data spanning January 1979 through August 2004; and (d) global precipitation variability obtained by averaging the 308 extracted harmonic amplitudes.

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those regions (Miller et al. 1994). Several secondary maxima can also be found in Fig. 1b—two are centered around New Zealand and 60°S, 230°E in the South Pacific, and the others appear as regional highs in tropical areas of the South and North Atlantic. Note that all of these maxima have largely zonal orientations, and those in the Pacific show a clear tendency towards being connected by meridional linkages basinwide. It is interesting to compare Fig. 1b with a similar map for global precipitation variability. To do so, the geographical distribution of the modal index derived in an earlier study using GPCP data (Chen 2006) is reproduced in Fig. 1d (along with global rainfall climatology in Fig. 1c). Surprisingly, to some extent the two patterns do not show any coupled or mirrored nature. Instead, an absolute maximum of rainfall variability is concentrated in the tropical Pacific. The single largest modal high is formed around 171°E at the equator, about 15° west of its SST counterpart. This suggests that the SST behaves like a multiengine system, while the precipitation behaves like a single-engine system. Such a characteristic reminds us to examine the cross correlation between SST climatology and precipitation variability, and vice versa. It turns out that a notable degree of cross similarity does exist (cf. Figs. 1a,d, as well as Figs. 1c,b), implying that the mean state of the ocean (atmosphere) is critical in driving the atmosphere (ocean). Air–sea interaction is established through climatology as a driving force on one side, and as a variable response on the other side. High SST variance may partially be attributable to persistent precipitation induced by severe weather conditions, such as storms, which systematically reduce the amount of solar radiation received at the sea surface, causing considerable variability in local SST. One of the key parameters in defining a significant mode is its central period. Figure 2 shows the period characteristics of SST variability associated with various spatial scales. The averaged harmonic amplitude as a function of extracting period for the global ocean, as well as the Pacific, Atlantic, and Indian Oceans, is plotted in Fig. 2a. The top eight globally significant peaks in Fig. 2a are identified, according to their central period, as primary modes (denoted as A–H in Table 3). A number of weaker but still significant peaks, corresponding to various oceanic regions, are identified as secondary modes (denoted as cd, fg, gh, and h* in Tables 4–6). In addition, decadal signals with larger uncertainties are also identified for comparison (marked as * and ** in Fig. 2 and Tables 4–6). Some form of significance test is needed to determine whether the principal modes obtained by using the 3D harmonic extraction scheme can be distinguished from

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those produced from a spatially and temporally uncorrelated random process. To do so, an evaluation technique based on the Monte Carlo simulation proposed by Overland and Preisendorfer (1982) is adopted in our analysis. Let dj ( j ⫽ 1, . . . , p) be the eigenvalue of the spatial correlation matrix computed from n datasets, such that d1 ⬎ d2 ⬎ . . . ⬎ dp. A normalized statistic can be computed as Tj ⫽ dj /(兺pj⫽1dj). We now use a random number generator to generate independent sequences of length n for p independent Gaussian variables of zero mean and unit variance and to compute the correlation matrix. The eigenvalues of the correlation matrix are then computed and the experiment is repeated, say, 100 times. If ␦ kj (k ⫽ 1, . . . , 100) is the set of eigenvalues produced by the kth Monte Carlo experiment, the statistic analogous to Tj is U kj ⫽ ␦kj/(兺pj⫽1␦kj). For fixed j, order the U kj so that U 1j ⱕ U 2j ⱕ . . . ⱕ U 100 j . 99 If Tj ⬎ U 95 (U 90 j j or U j ), then the jth eigenvalue is considered to be significant at the 95% (90% or 99%) level. The two statistics are estimated from a time series at each point on the grid and tested for their statistical significances. Identified modes are found to be statistically significant at different levels. For the global ocean, modes B, C, F–H are significant at the 0.01 level, while modes A, D, and E are significant at the 0.05 level. However, we could hardly evaluate the significance level of the decadal signals resulting from the small number of degrees of freedom on those time scales (i.e., they are not supposed to be statistically significant, but are merely indicative of the existence of relatively high energy at decadal time scales). For the three ocean basins, all of the major interannual modes are significant at the 0.01 level. In El Niño regions, primary modes within the ENSO band are significant at the 0.01 level, but those secondary modes are mostly at a lower significance level. The general pattern in Fig. 2a appears as a series of oscillations with a major peak at 12 months, corresponding to the annual cycle. Several other peaks are also evident (as marked with A through *), though with a much smaller amplitude. The global oscillation basically follows that of the Pacific, which is the largest for all periods longer than 24 months. The Indian Ocean is least dynamic at all periods, except for the semiannual cycle. The three ocean basins are generally consistent in the number of primary and secondary peaks they have; their specific locations, however, are considerably shifted, reflecting the geographically correlated nature of the natural SST modes. Specifically, we would like to have insight into the modal pattern in El Niño regions. Figure 2b shows the recovered SST amplitude as a function of period mostly

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FIG. 2. Spatially averaged harmonic amplitudes of SST as a function of extracting period. (a) The global ocean (black), and the Pacific (red), Indian (green), and Atlantic (blue) Oceans; (b) Niño-1 ⫹ -2 (red), Niño-3 (green), and Niño-4 (blue) regions; and (c) the NPDR (red), NADR (green), and SPDR (blue) are shown.

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TABLE 3. Characteristics of identified SST modes and variability maxima for the global ocean. Leading mode (%)#

Index

Period (month/year)

Relative order

Peak locations

A B C D E F G H * **

4 (0.3) 6 (0.5) 12 (1.0) 20 (1.7) 25 (2.1) 32 (2.7) 44 (3.7) 64 (5.3) 123 (10.3) 221 (18.4)

8 2 1 9 7 6 5 3 4 —

41°N, 179°E 18°N, 55°E; 42°N, 192°E 39°N, 120°E; 48°N, 298°E 1°N, 222°E 5°S, 283°E 5°S, 283°E 1°N, 206°E 6°S, 284°E; 0°, 200°E 36°N, 185°E; 47°N, 311°E; 0°, 183°E; 60°S, 231°E (56°N, 323°E)

#

0 3.2 94.8 0 0 0 0.1 1.5 0.4 0

— — — 11.2 2.0 6.9 12.8 21.1 28.0 18.0

Left and right subcolumns show percent coverage of the world’s oceans for all groups or the low-frequency groups (interannual or decadal), respectively.

within the ENSO regime for the three El Niño regions (see also Table 5 for a summary of their modal characteristics). Five striking oscillations with well-defined central periods are observed, and labeled D, E, F, G, and H. These modes are consistent in general oscillating cycles, but are slightly shifted in peak period. The leading mode for all three El Niño regions is found to be mode H, with a slowly decreasing period of 65, 64, and 62 months for the Niño-1 ⫹ -2, Niño-3, and Niño-4 regions, respectively. Inconsistency is observed when forming the second largest ENSO mode, which is mode F for Niño-1 ⫹ -2, and mode G for Niño-3 and Niño-4. It is worth noting that a subtle but significant mode is shared by the three El Niño regions with a period of 14–15 months, marked as “cd” in Fig. 2b. This mode is particularly evident in the Niño-1 ⫹ -2 region, but almost disappears outside the other El Niño regions. Figure 2c shows the recovered SST amplitude as a function of period for the North Pacific (NPDR), North Atlantic (NADR), and South Pacific decadal regions (SPDR; see also Table 6 for a summary of their modal characteristics). Inspection of Fig. 2c reveals more diTABLE 4. Identified SST modes and variability maxima for the Pacific, Indian, and Atlantic Oceans.

versity than consistency. Unlike the El Niño regions, there is little coherence in the three mode-rich regions beyond the annual cycle, confirming that locality is an intrinsic characteristic in mode generation. Three secondary modes emerge in Fig. 2c: modes fg, gh, and h* (see Table 6), which are mostly in the NADR. Meanwhile, modes G and H are missing in the same region. According to our regime division, modes A–C belong to the annual group (1–18 months) and modes D–H belong to the interannual group (1.5–8 yr). One has to keep in mind that, given the 221-month (18.4 yr) duration of the SST data used in this analysis, it is practically impossible to recover any decadal mode with a reasonable statistical confidence. In comparing our results (Tables 3–6) with those published in the literature (as summarized in Table 2), we find both agreements and departures as far as mode period is concerned. For the annual regime, previous and present studies all show sharp peaks at 12 and 6 months (Weare et al. 1976; Levitus 1987), suggesting the dominance of annual and semiannual cycles in global and hemispheric SST variTABLE 5. Identified SST modes and variability maxima for the three Niño regions. Period (month/year)

Period (month/year) Index

Pacific

Indian

Atlantic

A B C D E F G gh H h* *

4 (0.3) 6 (0.5) 12 (1.0) 20 (1.7) 25 (2.1) 32 (2.7) 44 (3.7) — 65 (5.4) — 129 (10.8)

4 (0.3) 6 (0.5) 12 (1.0) 20 (1.7) 25 (2.1) 31 (2.6) 44 (3.7) — 62 (5.2) — 103 (8.6)

4 (0.3) 6 (0.5) 12 (1.0) 20 (1.7) 25 (2.1) 31 (2.6) 44 (3.7) 56 (4.7) 61 (5.1) 76 (6.3) 116 (9.7)

Index

Niño-1 ⫹ -2 ( 0°–10°S, 270°–280°E)

Niño-3 (5°S–5°N, 210°–270°E)

Niño-4 (5°S–5°N, 160°–210°E)

A B C cd D E F G H *

4 (0.3) 6 (0.5) 12 (1.0) 15 (1.3) 17 (1.4) 26 (2.2) 32 (2.7) 42 (3.5) 65 (5.4) 180 (15.0)

4 (0.3) 6 (0.5) 12 (1.0) 15 (1.3) 17 (1.4) 26 (2.2) 32 (2.7) 43 (3.6) 64 (5.3) 133 (11.1)

— 6 (0.5) 12 (1.0) 14 (1.2) 18 (1.5) 25 (2.1) 30 (2.5) 44 (3.7) 62 (5.2) 126 (10.5)

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TABLE 6. Identified SST modes and variability maxima for the NPDR, NADR, and SPDR regions. Period (month/year)

Index

NPDR (30°–40°N, 180°–200°E)

NADR (40°–50°N, 300°–320°E)

SPDR (55°–65°S, 220°–240°E)

A B C cd D E F fg G gh H h* *

4 (0.3) 6 (0.5) 12 (1.0) — 22 (1.8) 27 (2.3) 32 (2.7) — 39 (3.3) — 67 (5.6) — 119 (9.9)

4 (0.3) 6 (0.5) 12 (1.0) 16 (1.3) 20 (1.7) 24 (2.0) 31 (2.6) 37 (3.1) — 53 (4.4) — 72 (6.0) 118 (9.8)

4 (0.3) 6 (0.5) 12 (1.0) 15 (1.3) 18 (1.5) 22 (1.8) 32 (2.7) — 44 (3.7) — 62 (5.2) — 137 (11.4)

ability. The 4-month mode uncovered here is, to our knowledge, previously unreported. Although this mode is very weak in intensity, it is spectrally distinguishable and is consistent for the three ocean basins (see Fig. 2a and Table 4). For the lower-frequency part of this regime, Jiang et al. (1995) reported a 15–16-month propagating oscillation in the equatorial Pacific. Jin et al. (2003) and Kang et al. (2004), among others, suggested that coupled ocean–atmosphere variability (a fast nearannual mode), with a period of around 12–18 months, exists in the tropical Pacific. Our result confirms the existence of this modal band, and further suggests that it consists of at least two submodes with a geographically varying period of 14–16 months (mode cd) and 17–20 months (mode D, see Tables 5, 6). The ENSO variability was described in some of the published works as an interannual mode within the ⬃2– 5-yr broad band (e.g., Weare et al. 1976; Hsiung and Newell 1983; Kawamura 1994; Lohmann and Latif 2005). Specific oscillation periods were also given by a number of investigators, for example, near 24 months (Rasmusson et al. 1990); 2.2, 2.8–3.0, 3.3–3.4, 4.3–4.8, and 5.1–5.7 yr (Mann and Park 1994); 24, 28, and 52 months (Jiang et al. 1995); 24–30, 45, and 60–65 months (Moron et al. 1998); 26 and 51 months (Zhang et al. 1998); and 43.7 months (Enfield and Mestas-Nuñez 1999). The identified ENSO-related primary modal periods in this study, that is, 18 (1.5), 25 (2.1), 32 (2.7), 43 (3.6), and 63 (5.3) months (years), cover most of the previous results, except for the 4.3–4.8-yr (including 51–52 months) mode. We have found a 53–56 month (4.4–4.7 yr) secondary mode (mode gh, see Tables 4, 6) in our analysis, but it appears to reside in the Atlantic rather

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than in the equatorial Pacific, as found by Jiang et al. (1995) and Zhang et al. (1998). It should be noted, however, that the 4.3–4.8-yr mode obtained by Mann and Park (1994), who used surface temperature data over large areas of North Pacific, North Atlantic, North America, western Europe, and central Asia, might be more comparable with our results.

4. Spatiotemporal characteristics of the natural SST modes a. Zonal and meridional pattern Period–latitude diagrams of harmonic amplitude recovered from SST are presented for four meridional sections in Fig. 3. The peaked feature displayed in all of the subplots confirms the high modality of global SST variability, and reveals much of its fine patterns. As expected, the dominant signals are seasonal cycles, especially the annual cycle at ⫾40° and the semiannual cycle near 40° and 20°N. Comparing the two central Pacific sections at 185° and 230°E (Figs. 3a,b), one finds that the decadal signal is better defined for the former, with two distinct realizations along the equator and 35°N, while the latter is richer in ENSO modes. A common feature shared by the two panels is that many of the ENSO modes and decadal variability maxima have their counterparts in the midlatitudes of both hemispheres. It serves as important evidence that some kind of teleconnection exists between the equatorial ocean and the North/South Pacific. This phenomenon might be related to earlier works by, among others, Tanimoto et al. (1993) and Lau and Nath (1996). Tanimoto et al. (1993) pointed out that the North Pacific SST anomalies with these time scales are strongly influenced by tropical atmosphere–ocean interactions, although they might be somewhat modulated by interactions within the extratropics. Lau and Nath (1996) suggested that atmospheric circulation plays an important role as a “bridge” in linking topical ENSO events to extratropical SST anomalies in the northern oceans. Our results further suggest that there is no obvious hemispheric preference for this tropical– midlatitude SST teleconnection, and most of the extratropical ENSO modes show a frequency shift toward a longer period for both hemispheres. This provides evidence in support of the argument that the global response to tropical SST forcing is inherently nonlinear (Hoerling et al. 1997). Previous studies have identified variability in the Pacific Ocean SST with a pattern similar to that of ENSO, but at lower frequencies. Zhang et al. (1997) documented that the dominant spatial signatures of SST, sea level pressure, and wind stress associated with interde-

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FIG. 3. Period–latitude diagrams of harmonic amplitude recovered from SST for sections at (a) 185°, (b) 230°, (c) 275°, and (d) 330°E.

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cadal variability may be described as being ENSO-like over the tropical and northern Pacific. A later study by Garreaud and Battisti (1999) extended the work of Zhang et al. (1997) to the Southern Hemisphere and found that interannual and interdecadal variability exhibit similar spatial signatures in the southern Pacific Ocean. In an analysis of last century’s sea surface temperatures, Chao et al. (2000) showed that an interdecadal oscillation with a period of 15–20 yr exists over the Pacific Ocean. They further revealed that the spatial structure associated with this Pacific interdecadal mode is roughly symmetric north–south across the equator, and is slightly distorted by the shape of the basin. Our result (Figs. 3a–d) is a good confirmation as well as a significant extension of their findings. The section at 275°E, which crosses the Niño-1 ⫹ -2 region, has a full collection of ENSO modes, with most of them staying slightly off the equator to the south (Fig. 3c). Among them, mode H (with a period of 65 months) is the dominant component. Figure 3d is plotted in order to diagnose the energetic signal with an approximate two-decade period (depicted as **), which possibly corresponds to the NAO in the high latitudes of the North Atlantic. It can be speculated that this mode exists with high intensity, although the present dataset is a little short for it to be fully resolved. Two zonal bands of particular interest in the SST modal analysis are in the equatorial and North Pacific, as revealed by Fig. 1b. Therefore, 35°N and 2°S are selected for plotting the period–longitude diagram (Fig. 4). The modal patterns appear to be much intensified in the Pacific sector, although weakened ENSO signatures, which look like the “shadows” of the Pacific ENSO modes, are easily discernible in both the Atlantic and the Indian Oceans, especially for modes F, G, and H (Figs. 4a,b). This is in support of a study by Enfield and Mayer (1997), who concluded that tropical Atlantic SST variability is correlated with Pacific ENSO variability in several regions. The major region affected is the North Atlantic area of the northeast trades west of 40°W along 10°–20°N, extending into the Caribbean. About 50%–80% of the anomalous SST variability there is found to be associated with the Pacific ENSO. A region of secondary covariability with ENSO can be observed along the northern edge of the mean ITCZ position, and it appears to be associated with the northward migration of the ITCZ when the North Atlantic warming occurs. A tropospheric temperature mechanism for the ENSO teleconnection was proposed by Chiang and Sobel (2002). It was argued that the tropical Pacific is the source region for tropospheric tempera-

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ture variations, that the signal spreads rapidly throughout the entire tropics, and that ENSO dominates the interannual variability of remote tropical tropospheric temperature. Evidence is shown that this mechanism is applicable to observed SST variability in the tropics outside the ENSO region. An impressive feature in Figs. 4a,b is that the ENSO modes in the three ocean basins all have a sharp horizontal orientation, while the decadal variability basically has broad vertical polarity. At 35°N, the leading mode is the annual cycle for almost all longitudes (Fig. 4a), followed by a strong decadal variability across the entire Pacific Ocean, whose central period varies from 100 to 150 months. A very weak signature of this feature is still visible in the central eastern Atlantic. In contrast, the predominant signals at 2°S are a series of distinct ENSO modes (Figs. 4b,c). They appear as well-separated tongue-shaped structures extending from the Peruvian coast toward the date line in the central Pacific. Note that this region is probably the heart of the ENSO engine, which has a full spectrum of ENSO modes. A gradual decrease in mode intensity (without cross-modal interference) from mode H to D, and from east to west, is apparent (Fig. 4c). In addition, an independent decadal signal is evident in the central Pacific, with two lobes in the western and eastern parts of the basin. Despite the fact that the decadal variability is marginally resolved at both 35°N and 2°S in the central Pacific, one should be cautious about it because the 221 months of SST data are certainly too short in terms of statistical significance. In an earlier study, Nakamura et al. (1997) reported spatial differences between a North Pacific decadal mode along the Kuroshio–Oyashio region and an ENSO-like mode to the north of Hawaii. In their result, the strongest decadal variability is found around the subarctic front, which is the most intense front in the basin extending zonally at 42°N. In contrast, the shortterm variability is the strongest to the east of the subarctic front in the western Pacific, and spreads much more broadly than the meridional extent of the individual frontal regions (see their Figs. 1a, b). As can be understood from the discussions herein, the results based on our methodology correspond generally well in many aspects with those produced by existing techniques, such as EOF (e.g., Kawamura 1994; Enfield and Mayer 1997; Nakamura et al. 1997), SVD (e.g., Mann and Park 1994), and SSA (e.g., Jiang et al. 1995; Chao et al. 2000). Obviously, however, the fine spatiotemporal patterns associated with natural SST modes as revealed by Figs. 3 and 4 represent a new perspective for examining the ENSO, PDO, and NAO behavior. These results clearly indicate that the description of events like ENSO will be significantly enhanced

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FIG. 4. Period–longitude diagrams of harmonic amplitude recovered from SST for sections at (a) 35°N and (b) 2°S; (c) a partial enlargement of (b) within the seasonal and ENSO frequency band is also shown.

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by knowing their intrinsic modal patterns (sub-ENSO modes) to a sufficient level.

b. Spatial patterns In this section, we are going to decompose Fig. 1b by inspecting the detailed spatial patterns at a number of selected SST periods (Fig. 5). The spatial distributions of modes A and B bear a certain degree of similarity (Figs. 5a,b), but they are considered to be modally independent, because their spectral peaks are separable (Fig. 2a), and such a similarity is not found with the 5-month harmonic (not shown). These short-period modes obviously have a Northern Hemisphere preference, with major semiannual maxima occurring in the Arabian Sea around 18°N, 55°E, in the North Pacific around 42°N, 192°E, and in the North Atlantic off of Newfoundland. These results agree well with Levitus (1987), who also found a 6-month maximum near 40°N, 195°E. The small difference (2°–3° in longitude and latitude) suggests that nearly two decades of satellite data are already comparable with 125 yr of in situ measurements in some aspects. A 13° westward shift is observed for the North Pacific maximum of the 4-month mode, compared to a similar feature of the 6-month mode (see Table 3). Mode C is the single most energetic mode (note the much larger color scale for Fig. 5c), which dominates the seasonal cycle of the ocean. SST seasonality is most prominent near the China Seas and Japan Sea in the northwest Pacific, and off the east coast of the United States and Canada in the northwest Atlantic. Large annual variability is also apparent in a circumpolar belt between 25° and 40°S in the Southern Ocean. The least dynamic region for seasonal SST change coincides with the western Pacific warm pool (Chen et al. 2004), where maximum SST climatology is found (Fig. 1a). Such a pattern of annual SST amplitude is in excellent agreement with Fig. 6 of Levitus (1987). The 15-month mode retains most of the features associated with the annual mode, but with two additional highs near 33°N, 186°E and the Niño-1 ⫹ -2 region (Fig. 5d). Figures 5e–i exhibit an absolute ENSO dominance in the El Niño regions, with both seasonal and decadal signals being largely suppressed. Although these ENSO modes are well separated in the frequency domain (see Figs. 2b and 4c), they turn out to be collocated in space as a converging westward extension zone along the equator, with its origin in the Niño-1 ⫹ -2 region. The geographical collocation of these modes prevents them, in many circumstances, from being individually identified. As a result, they were often considered as a single ENSO mode in some of the previous studies (e.g., Hsiung and Newell 1983; Kawamura 1994; Lohmann

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and Latif 2005). Our finding here has made it clear that ENSO-related SST modes have at least five components with well-defined periodicities and a high geographical correlation. In addition to the primary ENSO zone in the equatorial Pacific, a secondary high appears distinctly in the North Pacific at the following major ENSO frequencies: 35°–40°N, 195°E for modes F and H, and around Japan for mode G (Figs. 5g–i). These results are in general agreement with the dipole structure of ENSO-related variability at midlatitudes of the North Pacific, identified by Tanimoto et al. (1993) using 37 yr of COADS data. However, the exact locations of their ENSO modes (35°N, 210°E and 20°N, 135°E) have a considerable shift compared to our result (cf. their Fig. 3b with our Figs. 5g–i). Figure 5j carries information regarding the geographical location of the decadal variability, which has been extensively discussed since the 1990s (e.g., Tanimoto et al. 1993; Trenberth and Hurrell 1994; Zhang et al. 1997; Lohmann and Latif 2005). As can be seen, most significant decadal signals exist not only in the traditional PDO or NAO regions of the North Pacific and North Atlantic, but also in the Niño-4 region of the central equatorial Pacific, as well as in the eastern tropical Pacific and central southern Pacific. An elliptical monopole around 40°N, 180°, observed by Tanimoto et al. (1993), is nicely confirmed in our result (cf. their Fig. 3c with our Fig. 5j). In a recent study, Lohmann and Latif (2005) showed the coexistence of a decadal mode with a period of about 10 yr and an ENSO mode with a dominant period of about 4 yr in the tropical Pacific. Based on the results of in situ observation and model simulation, they argued that the variability of the shallow subtropical–tropical overturning cells is an important factor in driving the decadal mode. A joint comparison of Figs. 3a, 4b, 5i, and 5j leads to the conclusion that the decadal signal (*) and the ENSO modes (modes E–H) are fundamentally different in modal pattern (and, potentially, in generating mechanism), although they are close enough in space to be artificially linked. The inclusion of Fig. 5k in the end of the collection for the 221-month harmonic is to show that a potential interdecadal oscillation related to the NAO may start to emerge in the northwest Atlantic (Mizoguchi et al. 1999), though it still cannot be fully resolved with the near-two-decade dataset used in this analysis. As a summary of this subsection, the global distribution of the amplitude of the identified leading SST mode is shown is Fig. 6a. It is clear that an absolute majority (94.8%, see Table 3) of the world’s oceans are dominated by the annual cycle in SST variability. Surprisingly, this is true even for the most dynamic ENSO regions (Niño-1 ⫹ -2 and a large portion of Niño-3).

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FIG. 5. The spatial patterns of extracted SST harmonics at periods of (a) 4, (b) 6, (c) 12, (d) 15, (e) 18, (f) 26, (g) 32, (h) 43, (i) 64, (j) 123, and (k) 221 months are shown. The top color bar is for (c), the middle color bar is for (b), and the bottom color bar is for the remaining panels.

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FIG. 6. Global distribution of the amplitude of the identified leading SST mode shown for (a) all modal groups, and (b) the interannual and decadal groups only.

Semiannual signals are the most important in the Asian monsoon regions, such as the Arabian Sea, the Bay of Bengal, the Indonesian waters, and part of the equatorial Atlantic Ocean, representing a total of 3.2% of the world’s oceans. The ENSO modes G and H, together with the decadal signal *, only lead in a few small areas of the equatorial Pacific and Atlantic Oceans. It is therefore fair to argue that the SST variability is composed of one truly global mode, nearly a dozen regional modes, as well as numerous local modes. It is useful to examine the geographical distribution of the amplitude of the leading modes other than those with a high frequency (modes A–C), as shown in Fig. 6b. This time the relative importance of the lowfrequency modes exhibits an irregular pattern with the interannual modes (modes D–H) and the decadal variability maxima (groups * and **) leading in 54% and 46% of the global oceans, respectively (Table 3). Within the interannual group, mode H is dominant and occupies all El Niño regions and a large zonal area south of South America. The decadal group is the most energetic for a majority of the North Pacific and North

Atlantic, as well as the western parts of the South Pacific and South Indian Ocean.

c. Phase patterns Phase information is recovered, along with amplitude, following the procedures described in section 2 and Chen (2006), and is depicted at each grid point by the month of the period in which the maximum amplitude is reached. The obtained phase values are then normalized to a 12-month cycle to facilitate intercomparison. Finally, the normalized phase is binned into four quarters in order to capture major systematic oscillations, or the “seasons” for each harmonic. Figures 7a–k show the phase distributions corresponding to the associated amplitudes in Figs. 5a–k. The phase map of the 4-month mode is largely zonally banded with an alternating high–low pattern (Fig. 7a). The semiannual phase map is characterized by coherent warming in most of the extratropical oceans during the second quarter, and delayed peaking in the tropical oceans during the second and third quarters (Fig. 7b). It is interesting to see that an anticorrelation

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FIG. 7. The phase patterns of extracted SST harmonics shown at periods of (a) 4, (b) 6, (c) 12, (d) 15, (e) 18, (f) 26, (g) 32, (h) 43, (i) 64, (j) 123, and (k) 221 months. The phase values in all subpanels are normalized to 12 months and plotted on a quarterly basis.

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exists between the semiannual phase and the SST climatology (Fig. 1a). The 6-month peak propagates from cold polar regions to warm equatorial areas with a gradual phase delay. The annual phase pattern of SST shown in Fig. 7c has been described in detail in an earlier study by Chen and Quartly (2005). As can be seen, the phase distribution is basically hemispherically divided. The majority of the North (South) Pacific and North (South) Atlantic have their SST peak in boreal (austral) autumn. The Indian Ocean largely follows a Southern Hemisphere pattern in SST variation. Focusing on the equatorial areas of the Pacific and Atlantic Oceans, one finds two welldefined annual amphidromes with a cyclonic rotation, implying that the annual cycle (in time) of solar heating is translated into a rotary variation (in space) in sea surface temperature. Our results of the annual and semiannual harmonics indicate a notable improvement over similar ones produced by Levitus (1987), which appear to be much noisier, though some global agreement can still be found (see their Figs. 9, 10, 16, and 17). The defining feature in Fig. 7d for the 15-month mode is phase opposition between the two hemispheres. It basically precedes the annual pattern, with SST maxima reached during the second quarter for the Northern Hemisphere and the fourth quarter for the Southern Hemisphere. The equatorial oceans show up as a transitional zone with mixed phases. The phase maps of the ENSO modes are generally dominated by regional features, with the signature of the South Equatorial Current in common (Figs. 7e–i), suggesting that the SST evolves in the form of amplitude–phase coherence in the El Niño regions. Signatures of the western boundary currents can be recognized as systematic phase propagation in given modes, for instance, the Kuroshio and its extension in mode H (Fig. 7i), and the Gulf Stream and its extension in mode F (Fig. 7g). The phase pattern of the decadal signal * is dominated by large-scale features with high-value zones peaking during the second quarter for the North Pacific and the North Atlantic (Fig. 7j). A general phase reversal can be seen between the western–northern and central–eastern Pacific. For the 221-month harmonic, the phase pattern is again quite irregular on a global scale, with the exception of the North Atlantic (where its amplitude maximum is identified), which peaks largely in the third quarter (Fig. 7k).

d. Temporal evolution Next, we examine the temporal evolution of some of the natural SST modes by plotting the time series at

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their core locations along with the extracted amplitude as a function of period (Fig. 8). Going through the six panels on the left side in Fig. 8, one gets the impression that a variety of evolution patterns exists in the time domain. The semiannual mode in Fig. 8a (within the Arabian Sea) is obviously characterized by the occurrence of a secondary oscillation induced by the Asian monsoon within each annual cycle, resulting in a predominant 6-month peak, which is nearly 3 times higher compared to that of the 12-month one (Fig. 8b). Figure 8c (within the Bohai Sea) shows a classic annual mode with large regular amplitudes, which is representative of most of the extratropical areas. Figure 8e (within the Niño-1 ⫹ -2 region) basically retains the annual cycle, but with strong modulations during ENSO periods. Figures 8g,i (within the Niño-3 and Niño-4 regions, respectively) represent a typical pattern of interannual dominance as a result of the coexistence of several sub-ENSO modes plus a decadal oscillation (see also Figs. 8h,j). Figure 8k shows a reasonably regular annual SST cycle imposed on top of a secular trend for the northwest Atlantic Ocean (see also Fig. 8l). All of these results confirm the architectural complexity and geographical dependency of natural SST variability.

5. Summary and concluding remarks Taking advantage of a recently developed new methodology for extracting significant geophysical modes from a 3D space–time dataset, a systematic study on the recovery and analysis of natural modes in SST variability is carried out using the 1° ⫻ 1° gridded global Pathfinder data from January 1985 to May 2003. Given the nearly two-decade duration of this dataset, a range of SST modes or signals has been recovered at seasonalto-decadal time scales. Among them, eight of the most significant modes are identified as primary modes (Table 3), while four less significant ones are identified as secondary modes (Tables 4–6). In addition, two decadal signals with considerable variability have also been observed. The identified modes/signals fall into the following three regimes in period as well as in nature: the annual (1–18 months), interannual (1.5–8 yr), and decadal (beyond 8 yr) regimes. Such a classification ensures that the 2–7-yr-period band of ENSO is fully included in the interannual regime. In the first regime, our results confirm the classic annual and semiannual modes, with additional details; uncover the 4-month mode (mode A), with maxima in the northern Pacific; and extend the finding of a near-annual mode with a period of around

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12–18 months by further revealing that it consists of two submodes (modes cd and D), with peaking periods near 15 and 18 months. In the second regime, our results are consistent with the composite of previous ones in identifying the 18-, 25-, 32-, 43-, and 63-month SST modes (modes D–H) associated with ENSO, but are unable to provide firm evidence on the existence of a 52-month sub-ENSO mode in the Pacific Ocean. It should be pointed out that the primary limitation of this study is the short period of record examined. One also has to keep in mind the implicit assumption that the modes of variability identified in this analysis are basically stationary. In the third regime, our results agree with published ones in that the identified period of decadal signals in the northern and equatorial Pacific Ocean varies between 10 and 15 yr. We further demonstrate that decadal variability is rather common in the ocean; its central period and peak intensity, however, are sensitive to geographical locations (Fig. 8). A unique contribution of this study is the presentation of fine patterns of natural SST modes in joint dimensions of time, space, period, and phase. In doing so, we find that ENSO-related modes are well defined in the space–period domain. They appear as a series of modal maxima near the equatorial Pacific in the period–latitude diagram (with mode H being the most dominant one), and as a group of well-separated tongue-shaped westward extensions from the Peruvian coast toward the date line in the period–longitude diagram at 2°S. The degree of separability and regularity of these sub-ENSO modes is somewhat surprising, and thus reveals new details on the nature of this event. It is worth mentioning that the following three findings concerning the fine patterns of SST modes are of potential importance: the sharply opposed anisotropy between the interannual and decadal patterns (Figs. 4a,b), the occurrence of shadowed SST variability of the Pacific ENSO modes in both the Atlantic and Indian Oceans (Figs. 4a,b), and the existence of midlatitude counterparts of the equatorial interannual modes and decadal signals in both hemispheres (Figs. 3a,b). These features either suggest or confirm that (i) the decadal signal might be categorically different, given its much broader spectral peak compared to the ENSO modes; and (ii) teleconnection exists not only between the equatorial and extratropical regions as a result of the “atmospheric bridge,” but among the three ocean basins as well, for the same reason. Figures 5 and 6 are probably the first of their kind in the literature for which the spatial distributions of corresponding amplitude and phase are presented jointly for a dozen identified natural SST modes/signals at seasonal-to-decadal time scales. These results contain a

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significant amount of new information. The phase maps of the low-period modes are characterized by a zonally banded alternating high–low pattern for mode A, by an anticorrelation with the SST climatology for mode B, by well-defined amphidromes with a cyclonic rotation for mode C, and by a systematic hemispheric phase reversal for mode cd. Modes D through H are similar in spatial pattern, with the same origin in the Niño-1 ⫹ -2 region, and are internally coherent in phase evolution. The spatial overlap of these modes becomes a serious obstacle for identifying them individually using some of the space–time decomposition methods. This partially explains the reason that they are very often treated as a single ENSO mode within the ⬃2–7-yr-period band by some of the previous investigators. Our results also confirm the existence of a decadal SST variability maximum (as indexed with *), but indicate that it is mostly confined to limited zonal bands in the North Pacific and North Atlantic. Meanwhile, it is revealed that the decadal signal is also distinct in the Niño-4 region, as well as in some areas of the eastern tropical Pacific and central southern Pacific. Nevertheless, it is found that 98% of the world’s oceans have the annual or semiannual component as the leading SST mode, with only the remaining 2% dominated by the interannual modes G and H, as well as the decadal signal * (Table 3). However, if the annual group is taken out, the leading mode slightly favors the interannual group (54%) in terms of the area of occurrence compared to the decadal group (46%). The results obtained in this study clearly demonstrate that locality or regionality is a fundamental feature for the majority of SST (and probably other geophysical variables) modes. It implies that a decoupled space– time treatment would be ideal for extracting natural modes from SST variability. This is, in fact, one of the basic strategies behind our harmonic extraction scheme, in contrast to the simultaneous space–time decomposition strategy adopted by existing methodologies, like EOF, SVD, etc., which inevitably introduce some sorts of artificial constraints that may “damage” the natural variability. Ideally, if a geophysical variable varies coherently in a sufficiently large and regular spatial area, our methodology would probably produce very similar results to those of conventional methodologies. The main weakness of the adopted scheme is its inexplicit nature in characterizing the temporal evolution of identified modes compared to other techniques. Given the encouraging results obtained in our previous and present studies, it is hoped that a full range of natural modes of geophysical variability, from diurnal to centurial, will be revealed with the forthcoming

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FIG. 8. (left) SST time series and (right) their recovered harmonic amplitude as a function of extracting period at six peaking locations: (a), (b) mode B (18°N, 55°E); (c), (d) mode C (39°N, 120°E); (e), (f) modes E, F, and H (5°S, 283°E); and (g), (h) mode G (1°N, 206°E); and for (i), (j) harmonic * (0°, 183°E) and (k), (l) harmonic ** (56°N, 323°E).

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FIG. 8. (Continued)

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availability of fine-resolution, long-duration, and highquality data products. Not until then will the full potential and advantage of our proposed technique be convincingly demonstrated. Acknowledgments. This research was jointly supported by the National Basic Research Program of China under Project 2005CB422308, the Natural Science Foundation of China under Project 40675016, and the Chinese State Key Laboratory of Remote Sensing Science under an Announcement of Opportunity Project (2006). The editor and two anonymous reviewers are greatly appreciated for their helpful comments and constructive suggestions on an earlier version of this paper. REFERENCES Chao, Y., M. Ghil, and J. C. McWilliams, 2000: Pacific interdecadal variability in this century’s sea surface temperatures. Geophys. Res. Lett., 27, 2261–2264. Chen, G., 2006: A novel scheme for identifying principal modes in geophysical variability with application to global precipitation. J. Geophys. Res., 111, D11103, doi:10.1029/ 2005JD006233. ——, and G. D. Quartly, 2005: Annual amphidrome: A common feature in the ocean? IEEE Geosci. Remote Sens. Lett., 2, 423–427. ——, C. Fang, C. Zhang, and Y. Chen, 2004: Observing the coupling effect between warm pool and “rain pool” in the Pacific Ocean. Remote Sens. Environ., 91, 153–159. Chiang, J. C. H., and A. H. Sobel, 2002: Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate, 15, 2616–2631. Enfield, D. B., and D. A. Mayer, 1997: Tropical Atlantic sea surface temperature variability and its relation to El Niño– Southern Oscillation. J. Geophys. Res., 102, 929–946. ——, and A. M. Mestas-Nuñez, 1999: Multiscale variabilities in global sea surface temperatures and their relationships with tropospheric climate patterns. J. Climate, 12, 2719–2733. Frauenfeld, O. W., R. E. Davis, and M. E. Mann, 2005: A distinctly interdecadal signal of Pacific ocean–atmosphere interaction. J. Climate, 18, 1709–1718. Garreaud, R. D., and D. S. Battisti, 1999: Interannual (ENSO) and interdecadal (ENSO-like) variability in the Southern Hemisphere tropospheric circulation. J. Climate, 12, 2113– 2123. Hoerling, M. P., A. Kumar, and M. Zhong, 1997: El Niño, La Niña, and the nonlinearity of their teleconnections. J. Climate, 10, 1769–1786. Hsiung, J., and R. E. Newell, 1983: The principal nonseasonal modes of variation of global sea surface temperature. J. Phys. Oceanogr., 13, 1957–1967. Jiang, N., J. D. Neelin, and M. Ghil, 1995: Quasi-quadrennial and quasi-biennial variability in the equatorial Pacific. Climate Dyn., 12, 101–112. Jin, F.-F., J.-S. Kug, S.-I. An, and I.-S. Kang, 2003: A near-annual coupled ocean-atmosphere mode in the equatorial Pacific Ocean. Geophys. Res. Lett., 30, 1080, doi:10.1029/ 2002GL015983.

335

Kang, I.-S., J.-S. Kug, S.-I. An, and F.-F. Jin, 2004: A near-annual Pacific Ocean basin mode. J. Climate, 17, 2478–2488. Kaplan, A., M. A. Cane, Y. Kushnir, A. C. Clement, M. B. Blumenthal, and B. Rajagopalan, 1998: Analyses of global sea surface temperature 1856–1991. J. Geophys. Res., 103, 18 567–18 590. Kawamura, R., 1994: A rotated EOF analysis of global sea surface temperature variability with interannual and interdecadal scales. J. Phys. Oceanogr., 24, 707–715. Kilpatrick, K. A., G. P. Podestá, and R. Evans, 2001: Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res., 106, 9179–9198. Krishnamurti, T. N., D. K. Oosterhof, and A. V. Mehta, 1988: Air–sea interaction on the time scale of 30 to 50 days. J. Atmos. Sci., 45, 1304–1322. Lau, N.-C., and M. J. Nath, 1996: The role of the “atmospheric bridge” in linking tropical Pacific ENSO events to extratropical SST anomalies. J. Climate, 9, 2036–2057. Levitus, S., 1987: A comparison of the annual cycle of two sea surface temperature climatologies of the World Ocean. J. Phys. Oceanogr., 17, 197–214. Lohmann, K., and M. Latif, 2005: Tropical Pacific decadal variability and the subtropical–tropical cells. J. Climate, 18, 5163– 5178. Mak, M., 1995: Orthogonal wavelet analysis: Interannual variability in the sea surface temperature. Bull. Amer. Meteor. Soc., 76, 2179–2186. Mann, M. E., and J. Park, 1994: Global-scale modes of surface temperature variability on interannual to century timescales. J. Geophys. Res., 99, 25 819–25 834. Mestas-Nuñez, A. M., and D. B. Enfield, 1999: Rotated global modes of non-ENSO sea surface temperature variability. J. Climate, 12, 2734–2746. Miller, A. J., D. R. Cayan, T. P. Barnett, N. E. Graham, and J. M. Oberhuber, 1994: The 1976–77 climate shift of the Pacific Ocean. Oceanography, 7, 21–26. Mizoguchi, K.-I., S. D. Meyers, S. Basu, and J. J. O’Brien, 1999: Multi- and quasi-decadal variations of sea surface temperature in the North Atlantic. J. Phys. Oceanogr., 29, 3133–3144. Moron, V., R. Vautard, and M. Ghil, 1998: Trends, interdecadal and interannual oscillations in global sea-surface temperatures. Climate Dyn., 14, 545–569. Nakamura, H., G. Lin, and T. Yamagata, 1997: Decadal climate variability in the North Pacific during the recent decades. Bull. Amer. Meteor. Soc., 78, 2215–2225. Overland, J. E., and R. W. Preisendorfer, 1982: A significance test for principal components applied to a cyclone climatology. Mon. Wea. Rev., 110, 1–4. Rasmusson, E. M., X. Wang, and C. F. Ropelewski, 1990: The biennial component of ENSO variability. J. Mar. Syst., 1, 71–96. Rayner, N. A., P. Brohan, D. E. Parker, C. K. Folland, J. J. Kennedy, M. Vanicek, T. J. Ansell, and S. F. B. Tett, 2006: Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: The HadSST2 dataset. J. Climate, 19, 446–469. Smith, T. M., and R. W. Reynolds, 2004: Improved extended reconstruction of SST (1854–1997). J. Climate, 17, 2466–2477. Tanimoto, Y., N. Iwasaka, K. Hanawa, and Y. Toba, 1993: Char-

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acteristic variations of sea surface temperature with multiple time scales in the North Pacific. J. Climate, 6, 1153–1160. Trenberth, K. E., and J. W. Hurrell, 1994: Decadal atmosphereocean variations in the Pacific. Climate Dyn., 9, 303–319. Vazquez, J., K. Perry, and K. Kilpatrick, 1998: NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data set user’s reference manual, version 4.0. JPL Publication D-14070, 83 pp.

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Weare, B. C., A. R. Navato, and R. E. Newell, 1976: Empirical orthogonal analysis of Pacific sea surface temperatures. J. Phys. Oceanogr., 6, 671–678. Zhang, X., J. Sheng, and A. Shabbar, 1998: Modes of interannual and interdecadal variability of Pacific SST. J. Climate, 11, 2556–2569. Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability: 1900–93. J. Climate, 10, 1004–1020.