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  • View in gallery

    Percentage of months in which data was available at each grid point over the 480-month period analyzed.

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    Schematic representation of the South Atlantic upper-level ocean circulation (from Peterson and Stamma 1991).

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    (a) SST (in °C) climatology for 1953–92. Isotherm interval is 2°C. (b) SLP (in hPa minus 1000 hPa) and vector wind climatologies for 1953–92. Isobar interval is 3 hPa.

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    Spatial patterns E1(SST), E2(SST), and S3(SST) of the three EOF modes of SST. The spatial patterns are presented as homogeneous correlation maps; i.e., the contours are scaled such that the value at each grid point is the correlation coefficient between the time series of expansion coefficients of Fig. 5 and the SST anomaly at that grid point. Contour interval is 0.1. Negative contours are dashed. Zero line is thicker. Correlations higher than (in absolute value) (a) ±0.25, (b) ±0.21, and (c) ±0.16 are significant at the 95% level.

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    Time series of expansion coefficients e1(SST), e2(SST), and s3(SST) of the three EOF modes of SST. The time series are smoothed using a 13-month running mean (13MRM), and their amplitudes are normalized by the standard deviation.

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    Spatial patterns E1(SLP), E2(SLP), and S3(SLP) of the three EOF modes of SLP. The spatial patterns are presented as homogeneous correlation maps; i.e., the contours are scaled such that the value at each grid point is the correlation coefficient between the time series of expansion coefficients of Fig. 7 and the SLP anomaly at that grid point. Contour interval is 0.1. Negative contours are dashed. Zero line is thicker. Correlations higher than (in absolute value) (a) ±0.18, (b) ±0.16, and (c) ±0.13 are significant at the 95% level.

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    Time series of expansion coefficients e1(SLP), e2(SLP), and s3(SLP) of the three EOF modes of SLP. The time series are smoothed using a 13MRM, and their amplitudes are normalized by the standard deviation.

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    (a) Lagged correlations between e1(SLP) and e2(SST). SLP leads SST for positive lags. (b) Lagged correlations between e3(SLP) and e3(SST). SLP leads SST for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM).

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    (a) Lagged correlations between e3(SST) and SOI. SST leads SOI for positive lags. (b) Lagged correlations between e3(SLP) and SOI. SLP leads SOI for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM). The SOI monthly time series is smoothed by a 3MRM to be consistent with the other data.

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    Spatial patterns and expansion coefficient of the first SVD coupled mode of SST and SLP. Spatial patterns are homogeneous correlation maps. Contour interval is 0.1. Negative contours are dashed. Zero line is thicker. Correlations higher than (in absolute value) ±0.20 (SST) and ±0.19 (SLP) are significant at the 95% level. Time series are smoothed by a 13MRM. Amplitudes are normalized by the standard deviation.

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    Same as Fig. 10 but for the second SVD mode. Correlations higher than (in absolute value) ±0.25 (SST) and ±0.15 (SLP) are significant at the 95% level.

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    Same as Fig. 10 but for the third SVD mode. Correlations higher than (in absolute value) ±0.22 (SST) and ±0.16 (SLP) are significant at the 95% level.

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    Lagged correlations of the coupling coefficient r corresponding to each SVD mode. SLP leads SST for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM).

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    Correlation maps between s1(SLP) and SST gridpoint anomalies: (a) for SLP leading SST by two months and (b) for SLP lagging SST by two months.

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    SLP composites corresponding to extreme positive and negative values of s1(SLP). Isobars are in hPa minus 1000 hPa. Contour interval is 3 hPa.

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    Comparison of the South Atlantic first coupled mode with the North Pacific projection of the interdecadal joint mode of Mann and Park (1996).

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    Time series of monthly SLP anomalies at Gough Island (thin line), located at 40.4°S, 9.9°W, and s1(SLP) (thick line). Both time series are smoothed by a 13MRM, and the amplitudes are normalized by the standard deviation. A roughly 15-yr period oscillation is evident in both series.

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    Wind and SST anomalies correlated with s1(SST). The u-and v-components of the wind anomalies are correlated with s1(SST) at each grid point, and “vector correlations” are obtained by plotting the components in vectorial form. The SST pattern is the same as in Fig. 10 (top). The wind anomalies lead the SST anomalies by 1 month.

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    Same as Fig. 15 but using s2(SLP).

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    Same as Fig. 18 but for the second mode. SST and Wind correlations are simultaneous in this case.

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    Lagged correlations between the winter NAO index (December–March) and summer s2(SLP) (December–March). SLP leads NAO for positive lags. Dashed lines show the 95% significance levels. Lags are in years.

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    Same as Fig. 15 but using s3(SLP).

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    (a) Lagged correlations between s3(SST) and the SOI. SST leads SOI for positive lags. (b) Lagged correlations between s3(SLP) and SOI. SLP leads SOI for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM). The SOI monthly time series is smoothed by a 3MRM to be consistent with the other data.

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    Correlation maps between the SOI and (a) gridpoint anomalies and (b) gridpoint SLP anomalies.

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    Same as Fig. 18 but for the third mode.

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    (a) Lagged correlations between s3(SST) and the PNA index. SST leads PNA for positive lags. (b) Lagged correlations between s3(SLP) and the PNA index. SLP leads PNA for positive lags. Dashed lines show the 95% significance levels. Lags are in months.

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    Total SC and SC accounted for by the first three SVD modes from the observed SST and SLP datasets (asterisks) and from the 100 scrambled datasets (dots). All the SCs are normalized by dividing by the number of grid points of each variable (NM = 117 × 117).

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Atmosphere–Ocean Coupled Variability in the South Atlantic

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  • 1 Centre for Climate and Global Change Research and Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
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Abstract

The climate variability of the South Atlantic region is determined from 40 yr (1953–92) of Comprehensive Ocean–Atmosphere Data Set monthly sea surface temperature (SST) and sea level pressure (SLP) data using the empirical orthogonal function (EOF) and the singular value decomposition (SVD) analysis methods. The EOF method is applied to each field separately, whereas the SVD method is applied to both fields simultaneously. The significance of the atmosphere–ocean interaction is revealed by a strong resemblance between individual (EOF) and coupled (SVD) modes of SST and SLP. The three leading modes of coupled variability on interannual and interdecadal timescales are discussed in some detail.

The first coupled mode, which accounts for 63% of the total square covariance, represents a 14–16-yr period oscillation in the strength of the subtropical anticyclone, accompanied by fluctuations of a north–south dipole structure in the SST. The atmosphere–ocean coupling is strongest during the southern summer. The second coupled mode (20% of the total square covariance) is characterized by east–west shifts of the anticyclone center, in association with 6–7-yr period fluctuations of SST off the coast of Africa. The coupling depicted by this mode is weaker than that found in the first and third modes. The third coupled mode (6% of the total square covariance) is characterized by north–south displacements of the anticyclone, accompanied by SST fluctuations over a latitudinal band in the central South Atlantic. These oscillations occur on a relatively short interannual timescale (∼4 yr). As with the first mode, the atmosphere–ocean coupling is strongest during the southern summer. This mode is found to be temporally and spatially correlated with the El Niño–Southern Oscillation phenomenon. The statistical robustness of the results is tested by using a Monte Carlo approach, which indicates that the presented results are highly significant.

Corresponding author address: Dr. Lawrence A. Mysak, Centre for Climate and Global Change Research, McGill University, 805 Sherbrooke Street West, Montreal, PQ H3A 2K6, Canada.

Email: mysak@zephyr.meteo.mcgill.ca

Abstract

The climate variability of the South Atlantic region is determined from 40 yr (1953–92) of Comprehensive Ocean–Atmosphere Data Set monthly sea surface temperature (SST) and sea level pressure (SLP) data using the empirical orthogonal function (EOF) and the singular value decomposition (SVD) analysis methods. The EOF method is applied to each field separately, whereas the SVD method is applied to both fields simultaneously. The significance of the atmosphere–ocean interaction is revealed by a strong resemblance between individual (EOF) and coupled (SVD) modes of SST and SLP. The three leading modes of coupled variability on interannual and interdecadal timescales are discussed in some detail.

The first coupled mode, which accounts for 63% of the total square covariance, represents a 14–16-yr period oscillation in the strength of the subtropical anticyclone, accompanied by fluctuations of a north–south dipole structure in the SST. The atmosphere–ocean coupling is strongest during the southern summer. The second coupled mode (20% of the total square covariance) is characterized by east–west shifts of the anticyclone center, in association with 6–7-yr period fluctuations of SST off the coast of Africa. The coupling depicted by this mode is weaker than that found in the first and third modes. The third coupled mode (6% of the total square covariance) is characterized by north–south displacements of the anticyclone, accompanied by SST fluctuations over a latitudinal band in the central South Atlantic. These oscillations occur on a relatively short interannual timescale (∼4 yr). As with the first mode, the atmosphere–ocean coupling is strongest during the southern summer. This mode is found to be temporally and spatially correlated with the El Niño–Southern Oscillation phenomenon. The statistical robustness of the results is tested by using a Monte Carlo approach, which indicates that the presented results are highly significant.

Corresponding author address: Dr. Lawrence A. Mysak, Centre for Climate and Global Change Research, McGill University, 805 Sherbrooke Street West, Montreal, PQ H3A 2K6, Canada.

Email: mysak@zephyr.meteo.mcgill.ca

1. Introduction

During the past few decades there has been considerable effort devoted to obtaining a better understanding of natural climate variability on interannual to interdecadal timescales. To determine the mechanisms governing these climatic variations, it is essential to characterize the large-scale interactions between the ocean and the overlying atmosphere. A number of recent studies have sought to identify such interactions and their associated timescales, by analyzing both observational records and the results of climate model simulations.

The nature of the global air and sea surface temperature variability on interannual to interdecadal timescales has been documented in both data studies (Folland et al. 1984; Ghil and Vautard 1991; Houghton and Tourre 1992; Mann and Park 1993; Mann and Park 1994) and model simulations (Manabe and Stouffer 1994; Mehta and Delworth 1995). The relationships between sea surface temperature (SST) and sea level pressure (SLP) fluctuations and their coupled variability have also been widely investigated by analyzing a variety of datasets particularly over the North Atlantic and the North Pacific Oceans (Deser and Blackmon 1993; Kushnir 1994; Latif and Barnett 1996; Mann and Park 1996) and by performing experiments with atmospheric, oceanic, or coupled atmosphere–ocean models (Palmer and Sun 1985; Kushnir and Lau 1992; Delworth et al. 1993; Zebiak 1993; Peng et al. 1995; Halliwell 1996).

Thermodynamic and wind-induced mechanisms such as air–sea heat exchange, entrainment, vertical mixing, and wind stress forcing, have been frequently proposed as ways in which the atmosphere forces upper-ocean temperature anomalies (Frankignoul 1985; Wallace et al. 1990; Cayan 1992a; Cayan 1992b; Miller et al. 1994;Battisti et al. 1995). Other studies, however, have suggested that the ocean may drive atmospheric circulation anomalies through large-scale changes in the thermohaline circulation on interdecadal or longer timescales timescales (Levitus 1989; Ghil and Vautard 1991; Stocker and Mysak 1992). A few investigators have also proposed positive feedbacks between SST and the atmospheric circulation on interdecadal timescales (Deser and Blackmon 1993; Latif and Barnett 1994).

A common feature of these studies is that the major modes of large-scale variability in the Northern Hemisphere are characterized by interannual to interdecadal timescales. It has been generally accepted that interannual variability is primarily driven by the atmosphere, while interdecadal or longer-scale variability is mainly associated with changes in the ocean (Deser and Blackmon 1993; Kushnir 1994). However, recent work by Halliwell (1996) and results from this paper suggest that atmospheric forcing of the ocean on interdecadal timescales may be more important than previously realized.

As a complement to the Northern Hemisphere focus of the above studies, this paper (which is partly summarized in Venegas et al. 1996) investigates the natural climate variability in one region of the Southern Hemisphere, namely, the South Atlantic. The first goal is to identify the principal modes of behavior of the SST and the overlying atmospheric circulation, in order to provide insight into the variability of the South Atlantic coupled atmosphere–ocean system on interannual to interdecadal timescales. A secondary goal is to determine whether the South Atlantic modes of variability are connected with any of the well-known tropical and/or Northern Hemisphere climate oscillations (e.g., El Niño–Southern Oscillation, North Atlantic Oscillation, Pacific–North American pattern). The suggestion of such a connection between hemispheres on interdecadal timescales was proposed in Mysak and Power (1992). These connections were further explored by Mann and Park (1993, 1994).

Empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses are used to describe both the independent and coupled variability of SST and SLP in the South Atlantic. While the EOF analysis method has been widely applied in geophysics research (Davis 1976; Wallace et al. 1990; Deser and Blackmon 1993; Enfield and Mayer 1997), the SVD analysis method has become more commonly used only in the last decade (Lanzante 1984; Fang and Wallace 1994; Peng and Fyfe 1996), although it was first proposed in a meteorological context by Prohaska (1976).

The interest in trying to model the type of South Atlantic variability described in this paper has substantially increased since the creation of two recent large- scale climate programs: CLIVAR (Climate Variability and Predictability Program) and ACCP [The Atlantic Climate Change Program, described in Molinari et al. (1994)]. It is hoped that this observational study will offer new insights into the mechanisms and long-range linkages of interannual to interdecadal climate variability. These insights will also help the modeling studies associated with these programs.

The paper is organized as follows. A brief description of the datasets and methods, together with an introduction to the main South Atlantic oceanic and atmospheric features are given in section 2. Results from the independent SST and SLP EOF analyses follow in section 3. The results pertaining to the atmosphere–ocean coupling based on the SVD analysis are discussed in section 4. A summary and the conclusions are given in section 5.

2. Data and methods

a. Data

The data employed in this study include South Atlantic sea SST, sea level pressure (SLP), and vector wind extracted from the Comprehensive Ocean–Atmosphere Data Set (COADS) (Woodruff et al. 1987). These data are available as monthly means over a grid of 2° lat × 2° long, and span the period 1854–1992. The 40-yr period analyzed in this paper extends from 1953–92, during which time the data coverage over the South Atlantic basin is fairly good. The area of interest of our study is the South Atlantic Ocean, from the equator to 50°S, and from 70°W (or South American coast) to 20°E (or South African coast). The region south of 40°S between 40°W and 20°E is excluded from the analysis, however, due to poor data coverage (see Fig. 1).

The SST, SLP, and wind climatologies are calculated for each calendar month at each grid point by averaging the data over the 40-yr period. Monthly anomalies are then defined as deviations from this mean annual cycle. To reduce the noise inherent in the monthly observations, the anomalies are temporally smoothed by performing three-month running means (3MRM). At a given grid point, 3MRMs are computed by averaging anomalies of three consecutive months and by assigning that mean value to the central month. If two or more neighboring monthly anomalies are missing, the corresponding 3MRM is not computed and the month is given a missing value flag.

For a successful application of the EOF and SVD analysis methods described in the next subsection, the spatial gaps in the SST and SLP data are filled using an inverse distance interpolation method (Thiebaux and Pedder 1987). As a final step before applying the statistical methods, the data from the original 2° by 2° grid are averaged onto a coarser 4° lat × 10° long grid to reduce the size of the datasets used in our analysis.

b. Methods

Detailed discussions on EOF and SVD analyses can be found in Bretherton et al. (1992), Wallace et al. (1992), von Storch and Navarra (1995), and Newman and Sardeshmukh (1995). A brief description of both methods follows here.

EOF analysis is a statistical technique based on fundamental matrix operations. It has proven to be helpful in objectively identifying the principal (spatially uncorrelated) modes of variability of a given field. The analysis applies to a space- and time-dependent field with zero temporal mean. The covariance matrix of the field is constructed and diagonalized, resulting in a set of eigenvalues and corresponding eigenvectors. Each eigenvector (EOF) can be regarded as a spatial pattern (a map). To see how a given spatial pattern “evolves” in time, the eigenvector is projected onto the original field to obtain a time series (the expansion coefficient). Just as the EOFs are orthogonal in space, the associated time series are orthogonal in time. The fraction of the total field variance explained by a given EOF is proportional to its associated eigenvalue. Together, an eigenvalue with its corresponding EOF and expansion coefficient define a mode of variability. The leading mode (related to the largest eigenvalue) explains the largest fraction of the total variance, the second mode explains the largest fraction of the remaining variance, and so on.

SVD analysis can be thought of as a generalization to rectangular matrices of the diagonalization of a square symmetric matrix (i.e., EOF analysis). It is usually applied to two data fields together in order to identify pairs of coupled spatial patterns, which explain as much as possible the covariance between the two variables. The SVD of the cross-covariance matrix yields two spatially uncorrelated sets of singular vectors (analogous to the eigenvectors, but one for each variable) and a set of singular values associated with each pair of vectors (analogous to the eigenvalues). Each pair of singular vectors describe a fraction of the square covariance (SC) between the two variables. The first pair describes the largest fraction of the SC and each succeeding pair describes a maximum fraction of the SC that is unexplained by the previous pairs. The square covariance fraction (SCF) accounted for by the kth pair of singular vectors is proportional to the square of the kth singular value. The kth expansion coefficient for each variable is computed by projecting the respective kth singular vector onto each original data field. The correlation value r between the kth expansion coefficients of the two variables indicates how strongly related the coupled patterns are.

Both EOF and SVD analysis methods assume the data to be complete. When there are gaps in the data, and these are taken into account in the construction of the covariance matrix, the resulting modes are no longer strictly orthogonal. This problem is overcome here by filling the gaps as described previously. The differences in the results using the original and the interpolated datasets are, however, minimal. The analyses presented here are based on the interpolated data.

Correlation maps are used extensively in this study. These consist of gridpoint correlations between a time series (e.g., the expansion coefficient of an EOF mode, an index such as SOI, etc.) and gridpoint anomalies of a given field. Thus, correlation maps indicate how well the gridpoint anomalies of the field can be predicted from the knowledge of the time series. In addition, they provide a measure of the percentage of the variance explained locally by each mode, represented by the square of the correlations (Houghton and Tourre 1992).

The spectral analyses and coherences performed in this study are based on the multitaper method (Thomson 1982), and the confidence levels are obtained relative to an estimated red noise background (Mann and Lees 1996). Significance levels for all the correlations are estimated using the method described in Sciremammano (1979). This method accounts for the month-to-month autocorrelation inherent in the time series to compute the actual number of degrees of freedom before the estimation of significance levels. The 95% significance level is given in square brackets after each correlation coefficient. A Monte Carlo approach is used to determine the significance of the SST and SLP decompositions in modes of variability. A detailed description of the method is given in section 4d.

c. Mean South Atlantic conditions

Figure 2 depicts the ocean circulation features of the South Atlantic. The upper-level circulation is dominated by the South Equatorial Current, the Subtropical Gyre, and the Antarctic Circumpolar Current (ACC). The main components of the Subtropical Gyre are the warm and saline Brazil Current in the west and the cool and fresh Benguela Current in the east, joined together by the westward flowing South Atlantic Current. The cold and fresh Malvinas (or Falkland) Current originates as a branch of the ACC and meets the Brazil Current at the confluence zone (∼36°S).

The mean annual fields of SST, SLP, and vector wind are computed as the 40-yr (1953–92) climatologies from the COADS monthly means, and are shown in Fig. 3. The SST field exhibits a generally weak southeast–northwest gradient north of ∼35°S. The isotherms extend zonally and are more closely spaced south of this latitude. The SLP and wind fields are dominated by the South Atlantic subtropical anticyclone whose center is located near 30°S, 5°W. It is surrounded by southeasterly trades to the north and westerlies to the south. The following analyses of the nonseasonal variability in this study describe departures from these mean patterns.

3. The variability of SST and SLP: EOF analysis

The purpose of this section is to analyze independently the variability of the oceanic surface temperatures and the atmospheric sea level pressure during the 40-yr period using the EOF method. By doing this, we can compare the two sets of EOFs with the set of SVD modes obtained for the coupled atmosphere–ocean system in section 4. Only a brief discussion of the EOF (independent) modes of variability is given here, since this study concentrates on the SVD (coupled) modes.

a. Oceanic variability

An EOF analysis is performed on the unnormalized (i.e., not divided by the standard deviation) monthly SST anomalies over the South Atlantic Ocean for the period 1953–92. The three leading EOF modes together account for 47% of the total monthly SST variance. Individually, they explain 30%, 11%, and 6% of the variance. According to North’s rule of thumb (North et al. 1982), the difference between our third and fourth eigenvalues is comparable to the magnitude of their sampling errors, which means that the error in the EOFs is comparable to the size of the EOFs themselves. Hence, these two modes are not well separated and the decomposition between them may not be stable. Nevertheless, we keep the third mode since it will prove to be of interest in connection with searching for links to other well-known climatic fluctuations.

The spatial patterns associated with the first three SST modes are depicted in Fig. 4 as homogeneous correlation maps. They are, respectively, labeled E1(SST), E2(SST), and E3(SST). Figure 5 shows the temporal variability of each EOF, represented by the expansion coefficients e1(SST), e2(SST), and e3(SST), respectively.

E1(SST) (Fig. 4a) exhibits a monopole pattern extending over the entire domain. Recalling that the square of the correlation values represents the local variance explained (Houghton and Tourre 1992), this mode accounts for up to 64% of the variance in the region of largest loadings, namely, off the coast of southern Africa. The fraction of local variance explained decreases toward the south and west. Similar temporal and spatial structures of the first EOF mode of SST were found by Houghton and Tourre (1992) and by Enfield and Mayer (1997) in the common domain (0° to 20°–30°S) of their EOF analyses of the tropical Atlantic Ocean (30°N to 30°S).

E2(SST) (Fig. 4b) displays an out-of-phase relationship between temperature anomalies north and south of about 25°–30°S. This mode explains up to 20%–40% of the variance near its centers of action.

E3(SST) (Fig. 4c) exhibits three latitudinal bands of centers of action with alternating signs. A 20°-wide band centered around 25°S is surrounded by two bands of the opposite polarity north of 15°S and south of 35°S. The temperature fluctuations in the center of action near the coast of South Africa account for up to 20% of the total field variance.

The time series e1(SST) and e2(SST) (Figs. 5a,b) are characterized by a mixture of interannual and interdecadal fluctuations, while e3(SST) (Fig. 5c) oscillates on mainly interannual timescales.

b. Atmospheric variability

A similar EOF analysis is performed on the unnormalized monthly SLP anomalies over the same domain and time period as for SST. The first three EOF modes account for 59% of the total monthly SLP variance, while they individually explain fractions of 35%, 16%, and 8%. The same considerations as for SST apply here concerning the separation between the third and fourth modes. Once again, we keep the third mode since it is of particular interest to our results. Figure 6 shows the three spatial patterns E1(SLP), E2(SLP), and E3(SLP), as homogeneous correlation maps. Figure 7 shows their associated expansion coefficients e1(SLP), e2(SLP), and e3(SLP).

E1(SLP) (Fig. 6a) has uniform polarity over the entire domain. The center of action of the mode is located near the center of the subtropical anticyclone (see Fig. 3b). Hence, this mode describes the strengthening and weakening of the anticyclone. E2(SLP) (Fig. 6b) has a dipole structure, which is related to east–west changes in the location of the center of the subtropical anticyclone. Another dipole structure can be seen in E3(SLP) (Fig. 6c). In this case, changes in the position of the anticyclone occur in the north–south direction.

The time series e1(SLP) (Fig. 7a) is dominated by low-frequency (decadal-scale) oscillations, while e2(SLP) and e3(SLP) (Fig. 7b,c) exhibit predominantly interannual fluctuations.

c. Relating oceanic and atmospheric behaviors

To look for possible links between oceanic and atmospheric fluctuations, the correlation coefficients between the time series of the three discussed SST and SLP modes are calculated and displayed in Table 1. Correlations with the time series of the Southern Oscillation index (SOI) are also included to test for possible links with the El Niño–Southern Oscillation (ENSO) phenomenon. The SOI is defined as the normalized sea level pressure difference between Tahiti and Darwin. The table shows two significant correlations (at the 95% level) between SST and SLP modes and two significant correlations between ENSO and the third modes of SST and SLP.

The time series e1(SLP) and e2(SST) are significantly negatively correlated (−0.34), which is also evident from a visual comparison of the series in Figs. 7a and 5b. The fact that the first mode of the atmospheric variability is related to the second mode of the ocean variability suggests that the two are linked via atmosphere- to-ocean forcing, since we expect a strong signal to force a weaker one, and not the other way around. Lagged correlations between these two expansion coefficients (Fig. 8a) indicate that the two modes are best correlated (−0.36) when the atmosphere leads the ocean temperature by 1–4 months, consistent with the idea that atmosphere-to-ocean forcing links these two modes of variability.

The series e3(SLP) and e3(SST) are also significantly correlated (0.24). Lagged cross correlations between the two modes (Fig. 8b) exhibit two significant correlations. One of these shows the atmosphere leading the ocean by 2 months (0.30), suggesting again the presence of an atmosphere-to-ocean forcing. The other (at ∼−21 months) is probably a reflection of the ∼3.5-yr periodicity that exists in the two time series. That is, the two series are out-of-phase at a lag equal to half the period of oscillation (∼21 months).

The third modes of both SST and SLP are found to be significantly correlated with the SOI (−0.35). This suggests that ENSO has a discernible signal in the South Atlantic, even though these modes explain only a small percentage of the variability in their respective fields (6% and 8% for SST and SLP, respectively). Figure 9 shows lagged correlations between both third mode time series and the SOI. The highest correlations with ENSO are found when the variable (SST or SLP) leads the SOI time series by 1–3 months. Significant correlations at lag ∼21 months presumably have the same origin as those in Fig. 8b.

4. The variability of the coupled fields: SVD analysis

To better assess and confirm the relationships between SST and SLP variations obtained in section 3, a singular value decomposition (SVD) is performed on the cross- covariance matrix between SST and SLP. In contrast to the individual EOF analysis performed in the previous section, the SVD analysis on the two fields together will identify only those modes of behavior in which the SST and SLP variations are strongly coupled.

The three leading SVD modes of the coupled SST and SLP variations account for 89% of the total square covariance (TSC). Each mode independently accounts for 63%, 20%, and 6% of the TSC, respectively. We label the spatial patterns as Sk, and the expansion coefficients as sk, k = 1, 3. Table 2 displays the square covariance fractions (SCF) explained by each mode and the correlation coefficient (r) between the expansion coefficients of both variables [sk(SST) and sk(SLP)], as indicators of the strength of the coupling. The first mode exhibits a strong and highly significant coupling between SST and SLP. By contrast, the coupling coefficient associated with the second mode is smaller and closer to its significance level than those of the other two, which suggests a relatively weaker coupling. Even though the third SVD mode explains the least variance, its corresponding coupling coefficient is higher than that of the second mode. This reinforces our decision to include it in our discussion even though its associated singular value is not well separated from the fourth one (following North et al. 1982).

Seasonal means were computed from the monthly SST and SLP data and the SVD analysis was repeated to test the sensitivity of the results to the change of seasons. The southern winter is defined as the 6-month period from May to October, and the summer as the 6-month period from November to April. Table 3 shows the same parameters as Table 2 but after the seasonal breakdown of the data.

The coupling of the three modes is most significant during the southern summer, which is a somewhat surprising result. It is likely that the seasonality existent in the data coverage may account in part for the stronger coupling in summer. However, this argument should apply to studies on both hemispheres, and the majority of the Northern Hemisphere studies find the strongest atmosphere–ocean couplings during winter. Our interpretation is that the coupling in the Southern Hemisphere does not necessarily depend on the strong air–sea contrasts expected to occur during winter, since these are probably not as strong as in the Northern Hemisphere, mainly due to the different distribution of land and ocean coverage of the Southern Hemisphere. Instead, we argue that the coupling is strongest during summer because of possible links with major climatic oscillations observed in the Northern Hemisphere, which are strongest during the northern winter (NAO, PNA, etc.). Therefore, the signals in both hemispheres seem to show their strongest amplitudes simultaneously. This idea will be explored further in the next subsections.

A difference should be made, however, when analyzing the second mode in more detail. Although the simultaneous coupling is only significant in summer, a lagged correlation analysis of the seasonal coupling coefficients shows that the coupling is similarly significant in winter when SST leads SLP by one winter (0.51, [0.43], not shown). Hence, in contrast to the first and third modes, the second mode shows a similar degree of coupling in both seasons, although the forcing seems to act with different time lags in winter and summer.

The coupled spatial patterns and expansion coefficients corresponding to each variable and each mode are displayed in Figs. 10, 11, and 12. Lagged cross correlations between the SST and SLP time series associated with each mode are shown in Fig. 13. The three modes of coupled SST and SLP variability are discussed separately in the following sections.

a. First mode

Figure 10 depicts the spatial patterns and the expansion coefficient of the first SVD mode of the coupled fields. The SLP time series and pattern closely resemble those obtained for the first EOF mode of SLP (Fig. 6a). On the other hand, the SST time series and pattern for the first SVD mode resemble those of the second EOF mode of SST (Fig. 4b), with the signs reversed. Hence this mode is related to the correlation found between E1(SLP) and E2(SST) (Table 1). The correlation between s1(SLP) and e1(SLP) is 0.93 [0.22], while the correlation between s1(SST) and e2(SST) is −0.63 [0.31]. Thus, the EOF mode of SLP alone provides more information on the coupling between the two fields than the EOF mode of SST alone.

A lagged correlation analysis between s1(SST) and s1(SLP) (Fig. 13a) indicates that the coupling is strongest when SLP leads SST by 1–2 months (0.51 [0 29]), corroborating what we inferred from Fig. 8a. Therefore, the first SVD mode seems to depict an atmosphere-to- ocean forcing, in which the ocean response to the atmospheric changes appears with an intraseasonal time lag. According to Wallace and Jiang (1987), a signature of this kind of forcing is the marked asymmetry with respect to the zero lag exhibited in Fig. 13a. The significance of this asymmetry can be illustrated by contrasting the +2- and −2-month lag correlation maps between s1(SLP) and the gridpoint SST anomalies (shown in Fig. 14). In agreement with what we infer from Fig. 13a, the SST pattern obtained when SLP leads SST by two months (Fig. 14a) exhibits much larger amplitudes than its counterpart pattern (Fig. 14b) obtained when SLP lags SST by two months. This contrast clearly indicates that a maximum in the amplitude of the SST signal appears as a response to a prior maximum in the amplitude of the SLP signal. However, the opposite is not true, supporting the hypothesis of an atmosphere-to-ocean forcing.

To illustrate the behavior of this mode of variability in terms of atmospheric configurations, Fig. 15 shows composites of the pressure distribution of the months corresponding to the five highest and the five lowest values of s1(SLP). These composites describe the atmospheric conditions in the two extreme situations of the modal fluctuations. It is clear that this mode depicts an oscillation between strengthening and weakening of the subtropical anticyclone, accompanied by a slight displacement of the center toward the northeast in the weak anticyclone situations. The difference in the central pressure between both extrema reaches 6 hPa.

Spectral analyses of both s1(SST) and s1(SLP) (see Fig. 4 in Venegas et al. 1996) exhibit highly significant peaks at low frequencies (14–16 yr), indicating that interdecadal timescales dominate this mode. A comparison with the results obtained by Mann and Park (1996) suggests the possibility of a relation between s1(SLP) and their interdecadal joint mode in Northern Hemisphere surface temperature and sea level pressure. Figure 16 shows the South Atlantic mode s1(SLP) leading the Mann and Park interdecadal reference signal by approximately 4–5 yr. Even though the correlation between the two time series shown in Fig. 16 is not highly significant (0.89 [0.84] using a 5-yr running mean to smooth the data), partly due to the shortness of our time series, we speculate that there may exist a connection. Furthermore, Mann and Park’s analyses reveal that the Northern Hemisphere SLP anomalies associated with the interdecadal signal are pronounced only during the northern winter. This is simultaneous with the strongest expression of the South Atlantic signal during the southern summer (Table 3). An independent analysis of global surface temperature variability (Mann and Park 1994) shows a consistent global-scale temperature signal in phase with s1(SST), whose associated spatial pattern exhibits large SST variability near 35°S off the coast of South America, consistent with S1(SST).

The time series s1(SLP) is found to be highly correlated (0.55 [0.17]) with the SLP variations at Gough Island (40.4°S, 9.9°W) (as depicted in Fig. 17). This island is located just east of the center of action of S1(SLP) (Fig. 10c), therefore its SLP time series provides a useful index for describing the first mode of variability in the South Atlantic. Since these observations are independent from those of COADS, the correlation with s1(SLP) lends support to the existence of such oscillations in the strength of the subtropical anticyclone.

To investigate possible air–sea forcing mechanisms related to the first SVD mode, the wind anomalies associated with this mode are presented in Fig. 18 together with S1(SST) (same as Fig. 10a). The correlations are computed with the wind leading the SST by 1 month, since this lag was found to depict the strongest interaction between atmosphere and ocean (Fig. 13a). The first EOF mode of the wind anomalies over the South Atlantic (not shown) displays a distribution of wind anomalies similar to that observed in Fig. 18, indicating that such a wind pattern is a preferred mode of variability in the atmosphere.

The observed relationship between the wind and SST anomalies is mainly local. Stronger-than-normal trade winds (strong anticyclone episode) coincide with cool ocean temperatures in the northern part of the basin, while weaker-than-normal westerlies coincide to warm ocean temperatures in a band between 30°S and 40°S, lying west of 0° long. Also, southerly (northerly) winds are associated with negative (positive) SST anomalies. The local nature of the wind-SST relationship suggests that the anomalies in the ocean temperature are driven by wind-induced mechanisms (Wallace et al. 1990; Deser and Blackmon 1993; Kushnir 1994). These results also suggest that the atmospheric forcing of the ocean temperatures on interdecadal timescales may be more important than previously thought. Recent work by Halliwell (1996) dealing with the generation of SST anomalies in the North Atlantic supports this hypothesis.

Latif and Barnett (1994) have recently suggested that SLP and SST anomalies in the North Pacific distributed similarly to those in Fig. 10 could be in association with a positive feedback between atmosphere and ocean. Such an SST anomaly induces changes in the atmospheric circulation by modifying the north–south SST gradient and these atmospheric changes in turn reinforce the existent SST anomaly by air–sea heat exchange fluctuations. The resemblance between the SST and SLP patterns of Fig. 10 and their North Pacific SST and SLP patterns and the similar (interdecadal) timescales suggest that such a feedback may also be present in the South Atlantic. However, further work is needed to confirm this hypothesis.

b. Second mode

Figure 11 shows the components of the second SVD mode of the coupled fields. The SST time series and pattern closely resemble those of the first EOF mode of SST, with signs reversed (Fig. 4a). There is also some similarity between the SLP time series and pattern and those of the second EOF mode of SLP, again with signs reversed (Fig. 6b). The correlation coefficient between s2(SST) and e1(SST) is −0.91 [0.37], while that between s2(SLP) and e2(SLP) is −0.65 [0.14]. Thus, two facts seem to suggest that this mode of variability is associated with an ocean-to-atmosphere forcing: (i) the correlation betwen the two SST time series is stronger than that between the two SLP time series, and (ii) this coupled mode is associated with the first mode of independent (EOF) SST variability. Further support for this hypothesis comes from the lagged coupling correlation (Fig. 13b), which shows a significant simultaneous correlation between s2(SST) and s2(SLP) (0.30 [0.20]). The zero-lag correlation and the symmetry with respect to the zero lag depicted by this figure is suggestive of an essentially immediate adjustment of the atmosphere to changes in the ocean (Wallace and Jiang 1987).

The SLP composites in Fig. 19 are based on the five highest and lowest values of s2(SLP), and illustrate the behavior of the atmospheric component in this mode. An east–west displacement of the anticyclone center is clearly seen in these composites. A pressure trough appears (disappears) off the Argentine coast and the circulation becomes more meridional (zonal) when the anticyclone is at its easternmost (westernmost) position, which occurs at the highest (lowest) values of s2(SLP). The central pressure remains at the same value during the whole cycle.

Repeating the procedure used in section 4a to further investigate the atmosphere–ocean relationship, Fig. 20 displays superimposed wind and SST correlations with s2(SST). Both correlations are simultaneous here, since the coupling in this mode was found to be strongest at zero lag. To facilitate the interpretation, s2(SST) has been multiplied by −1. Stronger-than-normal trade winds are observed in the northeastern part of the basin over warmer-than-normal ocean temperatures. Thus, the SST-wind relationship cannot be explained by local wind-induced processes, since such processes would imply a negative SST anomaly being forced by anomalously strong winds (Wallace et al. 1990; Deser and Blackmon 1993; Kushnir 1994). Additionally, a lagged correlation between wind and SST indices computed at 0°, 10°S [the center of action of S2(SST)] shows no significant correlation for wind leading SST up to 10 months. This lack of evidence for wind driving the SST anomalies also supports the ocean-to-atmosphere forcing hypothesis.

The spectra of both s2(SST) and s2(SLP) (see Fig. 4 in Venegas et al. 1996) exhibit a significant peak at 6–7 yr. A spectral coherence (or cospectrum) analysis of s2(SLP) (only southern summer data) and the winter NAO index (as defined in Rogers 1984) exhibits a peak at the ∼5-yr period, significant at the 95% level (not shown). This period is consistent with the peak in the NAO spectrum at ∼7 yr found by Rogers (1984). On the other hand, a temporal lagged correlation between summer s2(SLP) and the NAO index suggests that the North Atlantic signal leads the South Atlantic one by ∼3 yr (Fig. 21). Even though this correlation is marginally significant at the 95% level, the evidence given by such correlations in the frequency and time domains suggest the possibility of a relationship between these North and South Atlantic modes of variability. Further work is needed to understand the nature of this connection.

c. Third mode

Figure 12 shows the components of the third SVD mode. The SLP and SST time series and patterns bare some resemblance to the respective third EOF modes (Figs. 4c and 6c), which were previously found to be correlated with one another (Table 1). The correlation coefficient between s3(SLP) and e3(SLP) is 0.55 [0.15] and that between s3(SST) and e3(SST) is 0.40 [0.18]. Both correlations are significant but much smaller than the ones obtained in the first two modes. Also in contrast to the previous modes, neither the SLP nor the SST coupled patterns appear clearly from the independent EOF analyses.

Figure 13c shows the lagged coupling correlations of this mode, with the highest values (∼0.42 [0.21]) occuring when SLP leads SST by 1–2 months. There is also a marked asymmetry with respect to zero lag in this figure, with significant correlations for SLP leading SST by up to 10 months. This suggests that this mode may be associated with an atmosphere-to-ocean forcing, as we discussed in section 4a (Wallace and Jiang 1987). Further support for this hypothesis comes from the comparison of the SST pattern with the wind anomalies related to this mode (Fig. 25). A westerly wind anomaly associated with the strong SLP north–south gradient around 25°S [see S3(SLP) in Fig. 12] produces a weakening of the climatological easterly winds (see Fig. 3b). The weaker-than-normal winds result in smaller-than- normal ocean-to-atmosphere heat fluxes, which produces a zonally oriented positive SST anomaly in a band around 25°S. Furthermore, a lagged correlation between SST and zonal wind indices computed at 25°W, 25°S [the center of action of S3(SST)] shows a significant correlation for zonal wind leading SST by 2 months (0.15 [0.11]), which reveals the local nature of the wind forcing.

The behavior of the atmospheric field in this mode is illustrated by the SLP composites shown in Fig. 22. A comparison of the two composites shows that a northward (southward) displacement of the high pressure center and stronger (weaker)-than-normal trade winds near the equator correspond to high (low) values of s3(SLP). The north (south) shift in the location of the subtropical anticyclone leads (by 1–2 months) the warming (cooling) of the ocean over the latitudinal band centered around 25°–30°S and extending from coast to coast (Fig. 12a).

Based on Table 1, we anticipate a relationship between this mode and ENSO. This is confirmed in Fig. 23, which shows lagged correlations of s3(SLP) and s3(SST) with the SOI. The highest correlations (−0.37, [0.20]) correspond to a 1-month lag, both SST and SLP leading SOI. Since the significant correlations are negative, and negative values of SOI roughly correspond to the mature phase of ENSO, this result suggests that a discernible warming in the central South Atlantic occurs ∼1 month before the maximum warming in the equatorial Pacific.

The spatial distributions of the SST and SLP anomalies in the South Atlantic during ENSO episodes are depicted in Figs. 24a,b by correlation maps between SOI and the grid point SST and SLP anomalies. The main centers of action displayed by these two maps are analogous to those of S3(SST) and S3(SLP) (Fig. 12), with the signs reversed. This lends support to the suggestion that this mode represents the ENSO signal in the South Atlantic. Similar ENSO-related SST and SLP spatial patterns are shown in Fig. 16.8 of Peixoto and Oort (1992).

The large southeasterly wind anomalies in the trades observed in the wind pattern associated with this mode (Fig. 25) are consistent with the findings of Enfield and Mayer (1997), who propose that the ENSO signal in the tropical Atlantic consists of a northward migration of the intertropical convergence zone (ITCZ) accompanied by strong southeasterly trades.

The power spectra of s3(SST) and s3(SLP) (see Fig. 4 in Venegas et al. 1996) exhibit significant peaks at ∼4 yr, which coincides with the typical ENSO period of oscillation. The spectral coherence between SOI and s3(SLP) confirms their correlation in the frequency domain, showing a highly significant peak at 4–5 yr (not shown)

The spectral coherence between s3(SLP) and the PNA index (as defined in Wallace and Gutzler 1981) has two significant peaks (not shown). The first one, at ∼4 yr, corresponds to the ENSO timescales and is consistent with the correlation between the positive phase of the PNA pattern and the equatorial Pacific warmings (Horel and Wallace 1981). Hence, this connection is likely a by-product of the fact that ENSO is the common denominator. The second one is a broad significant peak between 2.3 and 2.7 yr, which suggests a strong connection between this South Atlantic mode and the PNA on the quasibiennial timescale. Lagged temporal correlations of the PNA index with s3(SLP) and s3(SST) (Fig. 26) corroborate this connection, suggesting that the positive phase of the PNA (low pressure over eastern Pacific, high over North America, low over western North Atlantic) occurs ∼6–7 months after the northward shift of the South Atlantic subtropical anticyclone and the warming in the central South Atlantic.

A positive trend is also detectable in both s3(SST) and 3(SLP) (Fig. 12). This trend during the 40-yr period 1953–92 is in good agreement with the 90–100 yr oscillation found by Mann and Park (1994, Fig. 4). They suggest that a strong cooling trend over the northern North Atlantic from ∼1940 to the present is associated with a warming trend (of a much weaker amplitude) in the South Atlantic.

d. Statistical significance test

To asses the statistical robustness of the results obtained from the SVD analysis, we perform here a significance test using a Monte Carlo approach. The focus is on the SCs (the total square covariance, as well as that corresponding to each mode) rather than the SCFs (square covariance fractions) or the r coupling coefficients (Wallace et al. 1992). The SC is a direct measure of the coupling between the two fields, while the SCF and r are only meaningful when they are associated with a significant SC. We thus test the significance of the total SC between the SST and SLP anomalies (the sum of the squares of all the singular values) and the SC associated with the three first SVD modes (the square of the corresponding singular values).

The procedure is similar to that described in Wallace et al. (1992) and Peng and Fyfe (1996). We create a“randomized” SLP dataset by scrambling the SLP maps of the 40 yr in the time domain, in order to break the chronological order of SST relative to SLP. The scrambling is done on the years and not on the months; that is, the 12 SST maps of a given year will be paired with 12 SLP maps of another (randomly chosen) year, but keeping the order of the months inside the year. In this way, we keep the month-to-month autocorrelation inherent in the time series and do not deteriorate the intraseasonal variability, which would lower the significance levels and make our results appear more significant than they really are. We choose to scramble only the SLP dataset since it has a smaller month-to-month autocorrelation than the SST data, in order to minimize the increase of degrees of freedom inherent in the scrambling procedure.

We then perform an SVD analysis on the scrambled datasets, and repeat the same procedure 100 times. An SC value from the original datasets will be statistically significant at the 95% level if it is not exceeded by more than five values of the corresponding SC from the scrambled datasets. The results of the 100 scrambled SVD analyses are shown in Fig. 27, together with the results of the original SVD based on the observations. The total SC and the SC accounted for by the three modes in the original run are plotted with an asterisk, while the corresponding values of the 100 randomized runs are marked with small dots. The observed SC, SC1, and SC2 are found to be highly significant (at more than 99% level). Only two values of the scrambled runs exceeded the observed SC3, indicating that it is significant at the 98% confidence level.

In conclusion, the results discussed in this work are based on a statistically significant decomposition of modes of variability of the coupled SST and SLP fields.

5. Discussion and conclusions

The three leading EOF modes of variability of the individual SST and SLP fields in the South Atlantic region have been identified and analyzed. The leading modes of oceanic variability account for 47% of the total SST variance. A slightly larger percentage (59%) of the total SLP variance is accounted for by the leading modes of atmospheric variability. It is interesting to note that both decompositions leave a relatively large fraction of the total variance unexplained.

The principal features that characterize the individual modes of SST and SLP behavior are found again in the SVD of the coupled fields. The main patterns of variability of both variables independently provide considerable information on the coupling, but only one of the two variables dominates each of the two first coupled modes. While the first coupled mode keeps the characteristics of the main SLP (independent) pattern, the second coupled mode repeats the behavior of the main SST (independent) mode, suggesting the relative importance of each component of the system in each discussed coupled mode.

The first coupled mode of variability between the ocean temperature and the atmospheric pressure can be described as a strengthening and weakening of the subtropical anticyclone, which seems to force fluctuations in a north–south dipole structure in the ocean temperature by wind-related processes. The atmospheric forcing of the SST changes is detectable in the ocean with a lag of 1–2 months. The observed fluctuations are dominated by interdecadal timescales (14–16-yr period). The atmosphere-ocean coupling is found to be strongest during the southern summer. A possible relationship between this South Atlantic mode and interdecadal oscillations of Northern Hemisphere SST and SLP is also found. The possibility of an atmosphere–ocean feedback in the coupling depicted by this mode is suggested.

The second mode of coupled atmosphere–ocean variability in the South Atlantic is characterized by east–west displacements of the subtropical anticyclone center, accompanied by large SST fluctuations over a broad region off the coast of Africa, which occur on a 6–7-yr timescale. This mode seems to depict an immediate response of the atmospheric circulation to changes in the ocean, although the coupling between the SST and SLP fluctuations is rather weak. A significant frequency-domain correlation with the NAO index indicates that similar timescales characterize the fluctuations associated with these South and North Atlantic modes of variability.

The third South Atlantic mode is characterized by north–south displacements of the subtropical anticyclone, accompanied (with a lag of 1–2 months) by SST fluctuations over a latitudinal band in the central South Atlantic Ocean. Relatively high-frequency interannual timescales (∼4 yr) dominate the SST and SLP oscillations. Evidence is presented that suggests an atmospheric forcing of the ocean in this mode. However, the interaction between the ocean and atmosphere cannot be described by local wind-related mechanisms. The spatial patterns and temporal behavior of the SST and SLP anomalies are found to be strongly associated with ENSO. The ENSO-related signal in the South Atlantic leads the Pacific warmings by 1–2 months. A relationship with the Northern Hemisphere PNA atmospheric pattern is also suggested. A warming trend identified in this mode may be associated with a global century-scale oscillation.

The processes involved in the air–sea interactions depicted by these coupled modes of variability and the nature of the links between Southern and Northern Hemisphere fluctuations constitute the main subjects of investigation for further work.

Acknowledgments

We thank Halldór Björnsson for his input and assistance with the EOF and SVD analyses and Michael Mann for valuable discussions and help with the spectral analyses. Special thanks go to Yochanan Kushnir for kindly reviewing the first draft of the manuscript. We also thank Shiling Peng, John Fyfe, David Enfield, and the two anonymous reviewers for their help and suggestions. This work was supported by research grants awarded to LAM and DNS from the Canadian Natural Sciences and Engineering Research Council and Fonds FCAR (Quebec.)

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Fig. 1.
Fig. 1.

Percentage of months in which data was available at each grid point over the 480-month period analyzed.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 2.
Fig. 2.

Schematic representation of the South Atlantic upper-level ocean circulation (from Peterson and Stamma 1991).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 3.
Fig. 3.

(a) SST (in °C) climatology for 1953–92. Isotherm interval is 2°C. (b) SLP (in hPa minus 1000 hPa) and vector wind climatologies for 1953–92. Isobar interval is 3 hPa.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 4.
Fig. 4.

Spatial patterns E1(SST), E2(SST), and S3(SST) of the three EOF modes of SST. The spatial patterns are presented as homogeneous correlation maps; i.e., the contours are scaled such that the value at each grid point is the correlation coefficient between the time series of expansion coefficients of Fig. 5 and the SST anomaly at that grid point. Contour interval is 0.1. Negative contours are dashed. Zero line is thicker. Correlations higher than (in absolute value) (a) ±0.25, (b) ±0.21, and (c) ±0.16 are significant at the 95% level.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 5.
Fig. 5.

Time series of expansion coefficients e1(SST), e2(SST), and s3(SST) of the three EOF modes of SST. The time series are smoothed using a 13-month running mean (13MRM), and their amplitudes are normalized by the standard deviation.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 6.
Fig. 6.

Spatial patterns E1(SLP), E2(SLP), and S3(SLP) of the three EOF modes of SLP. The spatial patterns are presented as homogeneous correlation maps; i.e., the contours are scaled such that the value at each grid point is the correlation coefficient between the time series of expansion coefficients of Fig. 7 and the SLP anomaly at that grid point. Contour interval is 0.1. Negative contours are dashed. Zero line is thicker. Correlations higher than (in absolute value) (a) ±0.18, (b) ±0.16, and (c) ±0.13 are significant at the 95% level.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 7.
Fig. 7.

Time series of expansion coefficients e1(SLP), e2(SLP), and s3(SLP) of the three EOF modes of SLP. The time series are smoothed using a 13MRM, and their amplitudes are normalized by the standard deviation.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 8.
Fig. 8.

(a) Lagged correlations between e1(SLP) and e2(SST). SLP leads SST for positive lags. (b) Lagged correlations between e3(SLP) and e3(SST). SLP leads SST for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 9.
Fig. 9.

(a) Lagged correlations between e3(SST) and SOI. SST leads SOI for positive lags. (b) Lagged correlations between e3(SLP) and SOI. SLP leads SOI for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM). The SOI monthly time series is smoothed by a 3MRM to be consistent with the other data.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 10.
Fig. 10.

Spatial patterns and expansion coefficient of the first SVD coupled mode of SST and SLP. Spatial patterns are homogeneous correlation maps. Contour interval is 0.1. Negative contours are dashed. Zero line is thicker. Correlations higher than (in absolute value) ±0.20 (SST) and ±0.19 (SLP) are significant at the 95% level. Time series are smoothed by a 13MRM. Amplitudes are normalized by the standard deviation.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 11.
Fig. 11.

Same as Fig. 10 but for the second SVD mode. Correlations higher than (in absolute value) ±0.25 (SST) and ±0.15 (SLP) are significant at the 95% level.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 12.
Fig. 12.

Same as Fig. 10 but for the third SVD mode. Correlations higher than (in absolute value) ±0.22 (SST) and ±0.16 (SLP) are significant at the 95% level.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 13.
Fig. 13.

Lagged correlations of the coupling coefficient r corresponding to each SVD mode. SLP leads SST for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 14.
Fig. 14.

Correlation maps between s1(SLP) and SST gridpoint anomalies: (a) for SLP leading SST by two months and (b) for SLP lagging SST by two months.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 15.
Fig. 15.

SLP composites corresponding to extreme positive and negative values of s1(SLP). Isobars are in hPa minus 1000 hPa. Contour interval is 3 hPa.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 16.
Fig. 16.

Comparison of the South Atlantic first coupled mode with the North Pacific projection of the interdecadal joint mode of Mann and Park (1996).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 17.
Fig. 17.

Time series of monthly SLP anomalies at Gough Island (thin line), located at 40.4°S, 9.9°W, and s1(SLP) (thick line). Both time series are smoothed by a 13MRM, and the amplitudes are normalized by the standard deviation. A roughly 15-yr period oscillation is evident in both series.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 18.
Fig. 18.

Wind and SST anomalies correlated with s1(SST). The u-and v-components of the wind anomalies are correlated with s1(SST) at each grid point, and “vector correlations” are obtained by plotting the components in vectorial form. The SST pattern is the same as in Fig. 10 (top). The wind anomalies lead the SST anomalies by 1 month.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 19.
Fig. 19.

Same as Fig. 15 but using s2(SLP).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 20.
Fig. 20.

Same as Fig. 18 but for the second mode. SST and Wind correlations are simultaneous in this case.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 21.
Fig. 21.

Lagged correlations between the winter NAO index (December–March) and summer s2(SLP) (December–March). SLP leads NAO for positive lags. Dashed lines show the 95% significance levels. Lags are in years.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 22.
Fig. 22.

Same as Fig. 15 but using s3(SLP).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 23.
Fig. 23.

(a) Lagged correlations between s3(SST) and the SOI. SST leads SOI for positive lags. (b) Lagged correlations between s3(SLP) and SOI. SLP leads SOI for positive lags. Dashed lines show the 95% significance levels. Lags are in months. The lagged correlations are based on the original time series (not the ones smoothed by a 13MRM). The SOI monthly time series is smoothed by a 3MRM to be consistent with the other data.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 24.
Fig. 24.

Correlation maps between the SOI and (a) gridpoint anomalies and (b) gridpoint SLP anomalies.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 25.
Fig. 25.

Same as Fig. 18 but for the third mode.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 26.
Fig. 26.

(a) Lagged correlations between s3(SST) and the PNA index. SST leads PNA for positive lags. (b) Lagged correlations between s3(SLP) and the PNA index. SLP leads PNA for positive lags. Dashed lines show the 95% significance levels. Lags are in months.

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Fig. 27.
Fig. 27.

Total SC and SC accounted for by the first three SVD modes from the observed SST and SLP datasets (asterisks) and from the 100 scrambled datasets (dots). All the SCs are normalized by dividing by the number of grid points of each variable (NM = 117 × 117).

Citation: Journal of Climate 10, 11; 10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2

Table 1.

Correlation coefficients between the three first EOF modes of SST and SLP and between these modes and the SOI. Numbers in brackets are the 95% significance levels calculated for each correlation (Sciremammano 1979). Highly significant correlations are in bold face.

Table 1.
Table 2.

Square covariance fraction (SCF) and coupling correlation coefficient between the expansion coefficients of both variables (r) corresponding to the three leading SVD modes. In brackets are the 95% significance levels for the correlations.

Table 2.
Table 3.

Square covariance fraction (SCF) and coupling correlation coefficient (r) for the three SVD modes using 6-month winters and summers. In brackets are the 95% significance levels.

Table 3.
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