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  • View in gallery

    Time series of anomalies: (a) observed SST (145 yr), (b) model SST (200 yr), (c) observed SLP (92 yr), (d) model SLP (200 yr), (e) observed Parana River flow (96 yr), (f) model PSI (200 yr). Time series are smoothed with a 5-yr running mean and standarized

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    MTM spectra (solid line) and the 90%, 95%, and 99% significance levels (dashed, dot–dashed, dotted lines, respectively) for the (a) observed SST, (b) model SST, (c) observed SLP, (d) model SLP, (e) Parana River flow, and (f) model PSI

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    Standardized reconstructed component (period about 25–28 yr, thick black line) derived using SSA with a 40-yr window of the model time series shown in Fig. 1, overlaid by model time series (thin black line) smoothed with a 5-yr running-mean filter for (a) SST, (b) SLP, and (c) PSI. (d) The lagged correlation between the reconstructed components SST and SLP (solid line), SST and PSI (dashed line), and PSI and SLP (dot–dashed line). The first quantity leads the second for positive lags

  • View in gallery

    (top) SLP composites corresponding to the (a) positive and (b) negative phase of the SST RC index. (bottom) TAU composites corresponding to the (c) positive and (d) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

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    (top) PSI composites corresponding to the (a) positive and (b) negative phase of the SST RC index. (bottom) Meridional component of the ocean current field (V) composites corresponding to the (c) positive and (d) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

  • View in gallery

    (top) SST composites corresponding to the (a) positive and (b) negative phase of the SST RC index. (bottom) PPT composites corresponding to the (c) positive and (d) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

  • View in gallery

    Surface heat flux (°C cm s–1 × 105) composites corresponding to the (a) positive and (b) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

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South Atlantic Multidecadal Variability in the Climate System Model

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  • 1 Department of Physical Oceanography, University of São Paulo, São Paulo, Brazil
  • 2 Danish Center for Earth System Science, Niels Bohr Institute for Astronomy, Physics and Geophysics, University of Copenhagen, Copenhagen, Denmark
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Abstract

Strong multidecadal variability is detected in a 300-yr integration of the NCAR Climate System Model in the South Atlantic region, through the application of two signal recognition techniques: the multitaper method and singular spectrum analysis. Significant oscillations of a 25–30-yr period are found in the sea surface temperature, sea level pressure, and barotropic transport fields. A similar-scale signal is also captured in about one century-long observational records. A composite analysis of several model variables is performed based on the extremes of the sea surface temperature oscillation. The proposed mechanism for this basin-scale multidecadal signal involves changes in the intensity of the westerlies, associated with variability in the southward extension of the subtropical anticyclone, which drives changes in the ocean mass transport. This results in variability in the intensity of the Malvinas western boundary current and in the position of the Brazil–Malvinas confluence zone. Anomalous advection of cold waters northward (warm waters southward), owing to a strong (weak) Malvinas Current, is responsible for sea surface temperature anomalies in the subtropical western South Atlantic.

Corresponding author address: Dr. Ilana Wainer, Dept. of Physical Oceanography, University of São Paulo, São Paulo SP 05508-900, Brazil. Email: wainer@usp.br

Abstract

Strong multidecadal variability is detected in a 300-yr integration of the NCAR Climate System Model in the South Atlantic region, through the application of two signal recognition techniques: the multitaper method and singular spectrum analysis. Significant oscillations of a 25–30-yr period are found in the sea surface temperature, sea level pressure, and barotropic transport fields. A similar-scale signal is also captured in about one century-long observational records. A composite analysis of several model variables is performed based on the extremes of the sea surface temperature oscillation. The proposed mechanism for this basin-scale multidecadal signal involves changes in the intensity of the westerlies, associated with variability in the southward extension of the subtropical anticyclone, which drives changes in the ocean mass transport. This results in variability in the intensity of the Malvinas western boundary current and in the position of the Brazil–Malvinas confluence zone. Anomalous advection of cold waters northward (warm waters southward), owing to a strong (weak) Malvinas Current, is responsible for sea surface temperature anomalies in the subtropical western South Atlantic.

Corresponding author address: Dr. Ilana Wainer, Dept. of Physical Oceanography, University of São Paulo, São Paulo SP 05508-900, Brazil. Email: wainer@usp.br

1. Introduction

Decadal and multidecadal timescales seem to be prominent in the climate variability of the South Atlantic region, which may have significant impact on the coupled ocean–atmosphere interaction and predictability. Some recent studies (Venegas et al. 1997; Robertson and Mechoso 1998; Venegas et al. 1998; Reason 2000) have shown that variability on decadal and multidecadal timescales is an important component of the South Atlantic climate. Of particular importance are the basin-scale sea surface temperature (SST) changes associated with variations in sea level pressure (SLP) that ultimately impact the intensity of the gyre circulation transports. Ocean and atmospheric variability on interannual, decadal, and multidecadal timescales in the South Atlantic is not well understood. Some progress has been made both in terms of our understanding of the South Atlantic and our ability to observe it, but much remains uncovered, in particular regarding the sensitivity of the South Atlantic to climate change scenarios. The South Atlantic upper-layer circulation consists of a complex range of flows on a variety of spatial scales. The broad subtropical gyre is the dominating feature of the South Atlantic ocean circulation, and its mean flow is known to be basically wind driven. The South Atlantic is also believed to play a major role in the global ocean circulation through the thermohaline overturning cell. Surface waters flow northward in the upper layers of the Atlantic Ocean, becoming denser and sink in the northern high latitudes due to heat exchanges with the overlying atmosphere. Deep currents flow southward as part of the associated return flow. The underlying question is how the South Atlantic variability impacts global climate and on which timescales.

Recent observational and modeling studies focusing mainly on the SST and SLP fields in the Northern Hemisphere have shown that climate variability on decadal to multidecadal timescales may arise from coupled air–sea mechanisms in which the atmospheric and oceanic anomalies are maintained or damped by an adequate combination of positive and negative feedbacks. Examples of these studies with focus in the North Pacific include Latif and Barnett (1994, 1996), Robertson (1996), and Tourre et al. (1999a). On the other hand, the North Atlantic has been investigated by Kushnir (1994), Latif et al. (1996), Grötzner et al. (1998), and Tourre et al. (1999b), while studies on a hemispheric or global scale include Parker et al. (1994), Mann and Park (1994, 1996), and White and Cayan (1998). Similar coupled ocean–atmosphere mechanisms have been explored in the relatively fewer studies on multidecadal variability in the Southern Hemisphere. These include Reason et al. (1998), Jones and Allan (1998), and those mentioned in the beginning of this section.

Changes in the South Atlantic ocean circulation and SST patterns have been observed to occur over timescales ranging from subseasonal to multidecadal. These are strongly determined by interactions between the opposing flows of the Brazil Current and the Malvinas Current (Wainer et al. 2000), which in turn are affected by the basin-scale wind field and other atmospheric parameters. During the last few decades, climatic variations have had an important economic and social impact on the region [South Atlantic Climate Change (SACC) 1996]. Drought periods have produced changes in cattle population, drained the water supplies of large cities, and caused shortages of hydroelectric power. A significant relationship was found between rainfall anomalies over southern Brazil and Uruguay and SST anomalies in the Pacific and Atlantic Oceans (Diaz et al. 1998). This relationship is most pronounced during October–December and April–July. The years for which this link is strongest in the Atlantic do not necessarily coincide with those in the Pacific, which suggests that SST anomalies in the western Atlantic may contribute on their own to rainfall anomalies over southeastern South America. Several other studies also point out the importance of Atlantic SST in modulating rainfall over South America (Hastenrath and Heller 1977; Kalnay et al. 1986; Sperber and Hameed 1993; Wainer and Soares 1997). From a statistical point of view, Wainer and Soares (1997) discuss rainfall changes in northeast Brazil and their relationship not only with the position of the intertropical convergence zone (ITCZ), but also with rainfall variability in the Sahel region of Africa. By looking at patterns of correlation coefficient between monthly meridional wind stress anomalies, averaged for February, March, and April, and the Hastenrath index for precipitation anomalies for the period 1964–84, they show an out-of-phase relationship between these two regions, which is a function of the position of the ITCZ. Kalnay et al. (1986) found that low-level cyclonic vorticity values were associated with negative SST anomalies through the analysis of strong South Atlantic convergence zone (SACZ) events. This correlation indicates that the atmospheric anomalies are causing the SST anomalies, which in turn provide a negative feedback to the atmosphere. However, there are some indications that warm SST anomalies may also have a role in the onset of the convection over tropical South America. Clearly the possible influence of South Atlantic SST anomalies on the SACZ and convection onset on intraseasonal timescales should be further explored.

The annual runoff in eastern South America of the Negro, Paraguay, Parana, and Uruguay Rivers was also examined for the 83-yr period 1911–93 (Robertson and Mechoso 1998). Not only was a nonlinear trend detected in the river runoff time series, but quasidecadal and interannual components were detected as well. The trend and quasidecadal components are prominent in the Paraguay and Parana Rivers (central-eastern South America), with variability on interannual timescales (associated with El Niño–Southern Oscillation) most pronounced in the Negro and Uruguay Rivers farther south. The quasidecadal component of the Paraguay and Parana Rivers is strongest during the Southern Hemisphere summer. High river runoff on decadal timescales is associated with anomalously cold SST over the tropical North Atlantic. The most dramatic SST contrasts of the entire South Atlantic occur at its western boundary where the warm and salty waters of the southward-flowing Brazil Current meet the colder and relatively fresh waters of the northward-flowing Malvinas Current. The confluence zone between these two currents migrates north and south along the continental margin at seasonal (Wainer et al. 2000) and possibly longer timescales. These changes in turn impact on the atmosphere with likely effects on cyclogenesis and regional rainfall distribution. Therefore, although there is significant evidence for the importance of monitoring South Atlantic SST anomalies for regional climate and weather prediction in southern South America, much work is still needed if we want to understand how these SST anomalies affect rainfall regimes and climate patterns in the low-frequency regime, and how (or if) these in turn modify the SST distribution. Interdecadal variability with periods of about 15 years has also been detected in 80 years of South Atlantic SST and SLP observations (Venegas et al. 1998). The associated atmospheric patterns involve a slow propagation of SLP anomalies from east to west across the South Atlantic basin. The oceanic structure exhibit a north–south dipole in SST anomalies centered around 30°S.

The purpose of this study is to identify the predominant South Atlantic modes of variability in the National Center for Atmospheric Research (NCAR) Coupled Climate System Model (CCSM). A short description of the coupled model and of the observational data is presented in section 2 and the statistical methods employed are briefly described in section 3. Section 4 is devoted to the analysis of the spectral peaks and associated oscillatory components with multidecadal periods in the South Atlantic. A composite analysis of model atmospheric and oceanic fields is presented in section 5 and a sequence of mechanisms responsible for multidecadal variability in the South Atlantic is proposed. Section 6 summarizes and discusses the findings of this study.

2. Model description and observed datasets

The CCSM is a coupled, global general circulation ocean–atmosphere–land and sea-ice model developed at NCAR (Boville and Gent 1998). The atmospheric component of this model is the Community Climate Model, version 3 with T42 resolution (approximately 2.8° in lat and lon) and 18 vertical layers (Kiehl et al. 1998; Hack et al. 1998). The ocean component of this model was developed from the Geophysical Fluid Dynamics Laboratory z-coordinate primitive equation model (Gent et al. 1998). The spatial resolution is 2.4° in lon, with variable resolution in lat ranging from 1.2° to 2.3°, and 45 vertical levels. The meridional resolution is 2.2° at 20°S and 1.8° at 40°S. The sea-ice model dynamics is based on the cavitating fluid rheology by which the ice pack does not resist divergence or shear, but does resist convergence (Flato and Hibler 1992; Weatherly et al. 1998). The land surface model provides a comprehensive treatment of land surface processes allowing for different vegetation types (Bonan 1998). Although the land surface model computes river runoff, it is not transferred to the ocean model. The interaction between river runoff and shelf processes, a significant problem of human-scale relevance, has now been implemented in a later version of the coupled model. The annual cycles of wind stress, current transports, and SST associated with the Brazil–Malvinas confluence region in the CCSM have been exhaustively investigated (Wainer et al. 2000). Substantial variability on interannual and lower frequencies was found in this coupled simulation. Understanding and assessing its impact on regional climate in southern South America is the subject of this study.

The spatial characteristics of the South Atlantic annual and interannual variability in the CCSM have also been shown by Wainer and Gent (2001, Manuscript submitted to Climate Dyn., hereafter WaGe) to relate well with observations. They compared the spatial patterns of the first two SST and SLP EOFs from the CCSM with those from the Da Silva et al. (1994) observations. The first SST and SLP EOFs display an out-of-phase relationship between anomalies north and south of about 25°–30°S, while the second mode exhibits an east–west dipole structure with a change of sign near 20°W. The dominant patterns of interannual variability in the Da Silva et al. (1994) dataset are well captured by the CCSM, and their amplitudes are comparable. A similar spatial structure is found in the first two EOFs of the CCSM ocean barotropic streamfunction (PSI). Furthermore, the relationships between atmosphere and ocean in the CCSM were investigated by means of lagged correlations between the EOF time series of SLP, SST, and PSI. Such correlations were shown to be highly significant at zero lag between SLP and PSI, and at a lag of 1–2 months between SLP and SST. Similar results were obtained from the Da Silva et al. (1994) dataset and in the analysis of Venegas et al. (1997). The analysis presented by WaGe confirms that the CCSM has realistic interannual variability in the South Atlantic and provides a complement to the study of Saravanan (1998), who validated the CCSM in the North Atlantic and North Pacific. Significant correlations are also found between the CCSM SST and precipitation first EOFs, again with a lag of 1–2 months.

The model data consist of the monthly mean output of SST, SLP, and PSI from the last 200 years of a 300-yr control run of the NCAR CCSM [Boville and Gent (1998)]. These data have their seasonal cycle removed by subtracting the full 200-yr climatology. In addition, basinwide fields of model anomalies are analyzed for SST, SLP, PSI, the meridional component of the ocean current field (V), the zonal and meridional components of the wind stress (TAU), and precipitation (PPT). The South Atlantic region, from 80°W to 20°E and from the equator to 55°S, was extracted from the global fields.

Observed historical data is used to validate the model data. SST observations are obtained from the Met Office Historical Sea Surface Temperature Anomalies dataset (MOHSST5, ATLAS7) from the Met Office. Reduced space optimal estimation has been applied in order to produce 145 years (1856–2000) of global SST anomalies from the 1951–80 climatology (Kaplan et al. 1998). SLP data are obtained from the Global Monthly Sea Level Pressure dataset (GMSLP2) available from the Hadley Centre at the Met Office (Basnett and Parker 1997) and cover the 92-yr period 1903–94. Streamflow data of the Parana River for the 96-yr period 1904–99 are also used (Robertson and Mechoso 1998). This flow is measured at Corrientes (27°S, 58°W), 50 km south of the confluence of the Parana and Paraguay Rivers.

3. Statistical analyses

The statistical methods used to identify and evaluate multidecadal variability in the model and in the observations consist of the multitaper method (MTM) and the singular spectral analysis (SSA) techniques. These are implemented using the SSA–MTM Toolkit (Dettinger et al. 1995; Ghil 1997) and FORTRAN routines for MTM spectral estimation (kindly provided by M. Mann, University of Virginia, 2000 personal communication). The methodology followed in this study is similar to that used by Robertson and Mechoso (1998) to identify interannual and decadal periodicities in river flows in South America. The application of the two complementary methods of spectral analysis permits us to assess the existence and robustness of oscillatory components in the data in two independent ways.

As a first step to detect significant signals in a short time series, an estimation of its spectral density is obtained using the MTM method (Percival and Walden 1993). This is done by multiplying the time series by a small number of orthogonal window functions or data tapers. The tapered time series are then Fourier transformed, resulting in a set of independent spectral estimates. The ensemble average of these spectral estimates provides a final spectrum with an optimal trade-off between spectral resolution and variance. This procedure reduces the variance of the spectral estimate and allows for the description of structures in the time series that are modulated in frequency and amplitude. Statistical significance is determined against a red noise null hypothesis using a robust estimation of background noise (Mann and Lees 1996).

Once the significant spectral peaks have been identified using MTM, SSA is applied to reconstruct the dominant oscillatory components of the time series (Vautard et al. 1992; Allen and Smith 1996; Ghil et al. 2001). The SSA technique is a variation of the classical empirical orthogonal function analysis, in which the decomposition is performed in the time–lag domain instead of in time–space. It enables the analysis of a time series through a moving window in order to detect the temporal patterns that account for most of the variance in the series. SSA decomposes a time series into its oscillatory components through an eigenvalue decomposition of the autocorrelation matrix, making the moving windows highly data adaptive. Each oscillatory component is associated with a pair of nearly equal eigenvalues and in-quadrature eigenvectors, from which the oscillatory part of the time series can be reconstructed. By doing so, SSA attempts to overcome the problems relative to signal detection from the background climate variability by separating the deterministic periodic oscillations in the data from random, nonoscillatory noise processes. A set of sensitivity tests have been performed using different widths of the moving windows (ranging from 30 to 60 yr), which yielded very similar results. The reconstructed components presented here have been computed using a window width of 40 years.

4. Identifying the multidecadal variability

Most of the studies mentioned in the introduction suggest that a considerable fraction of the low-frequency variability in the subtropical South Atlantic can be associated with changes in the intensity and southward extension of the subtropical anticyclone, from the atmospheric point of view, and to opposite-signed SST anomalies to the north and south of the SACZ, more pronounced near the western boundary, from the oceanic point of view. There are regions, however, which are not directly affected by these mechanisms. Examples of these are the Agulhas retroflection region, in the southeast Atlantic, in which interocean exchanges from the Indian Ocean represent an important source of interannual and decadal variability, and the high-latitude South Atlantic in which variability is mainly linked to changes in the Antarctic Circumpolar Current (ACC) and in Antarctic sea ice. A strong annual signal in the CCSM western subtropical South Atlantic was detected both in numerical studies (Wainer et al. 2000) and in altimetry data (Goni and Wainer 2001). Variability in the latitude at which the Brazil and Malvinas Currents turn eastward and separate from the shelf break leads to large SST variations, both on seasonal and interannual timescales.

Earlier South Atlantic investigations (Venegas et al. 1997; WaGe) have revealed significant variability in SST and SLP in a zonal band around 40°S. The largest variance in SST (of the order of 10°C), however, is found in the westernmost part of this zonal band. Based on those findings, time series of observed and model SST and SLP anomalies are constructed as zonal averages at 40°S from the South American coast to 20°E (Fig. 1). Also shown in Fig. 1 are the time series of anomalies of the Parana River flow with respect to its mean seasonal cycle, and the model PSI anomalies averaged along 40°S between 65° and 30°W, the region in which PSI exhibits largest variability (WaGe). The smoothed time series reveal well-defined low-frequency variability associated with both model and observed data. It should be noted that the SST and SLP variability depicted by these time series are not substiantally sensitive to the definition of the eastern boundary of the zonal average, as concluded from a series of sensitivity studies performed (not shown). In the following, we will attempt to isolate and quantify the timescales of variation in these time series by using the spectral analysis methods described above.

The MTM spectra of the six time series are shown in Fig. 2. Each spectrum was computed as the average of three spectral estimates, obtained by premultiplying the data by a family of orthogonal data tapers. The bandwidth parameter used is p = 2, which gives a full-bandwidth spectral resolution (2pfR) of 0.02 cycles per year (cpy) for the 200-yr-long time series of model data and of around 0.04 cpy for the one century-long observational time series [here fR = (NΔt)–1 is the Rayleigh frequency, where N is the number of time steps and Δt is the sampling interval]. Such a spectral resolution allows us to resolve multidecadal from decadal and shorter timescales in the MTM spectra.

We are particularly interested in the multidecadal band as little is known about the low-frequency modes of climate variability in the South Atlantic region. A distinct feature in the MTM spectra of the three model variables (Figs. 2b,d,f) is the statistically significant spectral peak at 25–30 years (frequency around 0.033–0.04 cpy). A similar peak is seen in the observed SST spectrum (Fig. 2a), though at a slightly shorter period (closer to 22 years, frequency around 0.045 cpy). The observed SLP and Parana River flow spectra (Figs. 2c,e), however, present significant peaks at slightly longer periods (closer to 40 years, frequency around 0.025 cpy). The shortness of the observational records (roughly a century) does not allow for a more precise determination of the period of such low-frequency fluctuations. Despite the small discrepancies in the exact period of oscillation, both observed and model data show a clear indication of significant variability on multidecadal timescales.

Independent assessment of the significance of the 25–30-yr periodicity detected in the MTM spectra is provided by the application of the SSA technique to the 3 model time series (SST, SLP, and PSI). Using a window width of 40 years, we find that the 2 leading eigenvalues for each time series are nearly equal and that their associated eigenvectors form an oscillatory pair. The reconstructed components (RCs) calculated from the two leading SSA modes (Fig. 3) exhibit a dominant period of oscillation of 25–28 years for the 3 variables. The RCs are narrowband-filtered versions of the original data, where the filters are determined from the data itself so as to maximize the explained variance. They represent the contribution of the multidecadal oscillation to the total variance of the original time series. This contribution is around 3% for SST, 9% for SLP, and 17% for PSI. On the other hand, correlation coefficients between the RCs and the smoothed time series (Fig. 3) are 0.63 for SST, 0.52 for SLP, and 0.80 for PSI (significant at the 95% level). Hence, the SSA technique provides an independent estimate of the significance of the 25–30-yr timescale in the studied time series.

The 25–30-yr oscillations of the 3 fields are significantly correlated with one another. The cross-correlation coefficients CC between the different RCs at different lags are shown in Fig. 3d. SLP leads SST by approximately 2.5 years [CC(SST, SLP) = 0.78], PSI leads SST by approximately 1 year [CC(SST, PSI) = −0.81], and SLP leads PSI by approximately 1.5 years [CC(PSI, SLP) = −0.48]. All correlations are significant at the 90% significance level. The strong relationships between SST and both SLP and PSI are indicators of ocean dynamics being an important mechanism for driving the variability on this timescale. The atmospheric circulation (represented by SLP) modulates the intensity of the subpolar gyre through the associated geostrophic wind field. This in turn affects the transport (and exchange) of warm water toward high latitudes and cold water toward low latitudes within the Brazil–Malvinas confluence (BMC) region, thereby producing SST changes in this region. Further support to this hypothesis will be provided by the composite analysis in the following section.

5. Composite analyses

The SSA-derived SST RC (Fig. 3a) is used as an index to composite the CCSM anomalous fields. These composites illustrate the spatial coherence between the multidecadal SST variations and related atmospheric and oceanic fields and characterize the two extreme phases of the 25–30-yr cycles. Positive-(negative) phase fields were obtained by compositing SST, SLP, PSI, V, TAU, and PPT for the months in which the amplitude of the SST RC exceeds ±1 standard deviation. Figures 4–6 show both the positive and negative phases of the 25–30-yr cycles for the 6 variables. The spatial structures of the SST, SLP, PSI, and PPT composites resemble very closely those of their respective first spatial EOFs (WaGe).

The amplitude of the climatological model SST and PSI fields were compared with the observed ones by Wainer et al. (2000) when possible. It is relatively easy to compare model SST with observations, given the number of reliable datasets available. PSI values were compared with the few available hydrographic data. Wainer et al. (2000) show for example, that a maximum transport value in the CCSM of 31 Sv (1 Sv ≡ 106 m3 s–1) is found in February–March with a minimum value of 18.5 Sv in June. These values are consistent with the simulated transport estimates obtained by Matano et al. (1993). They also agree with hydrographic and inverted echo-sounder data near 38°S, obtained by Gordon (1989), Gordon and Greengrove (1986), and Garzoli and Giulivi (1996), which vary between 19 and 22 Sv depending on the time of year. A comparison of the annual cycle of SST, zonally averaged between 65°W and 15°E, from the CCSM and from the Reynolds and Smith (1994) observations was also performed by Wainer et al. (2000). The phase and amplitude of the annual cycle is the same in both, with maximum temperatures at the end of summer (February–March) and minimum temperatures at the end of winter (August–September). The same phase and amplitude of the annual cycle also appears in the Da Silva et al. (1994) climatology. Therefore, the small amplitudes shown in the SST and PSI composites are just a result of the averaging process.

The proposed mechanism of ocean–atmosphere interaction on the multidecadal timescale is as follows. A SLP increase in the region along 40°S (Fig. 4a) implies a southward extension of the subtropical anticyclone with an associated weakening of the climatological westerlies along 40°S (Fig. 4c). The weak atmospheric zonal circulation leads to a weakening of the subpolar gyre transport (PSI, Fig. 5a). This implies the weakening of the eastward-flowing South Atlantic Current and the northward-flowing Malvinas Current (on the northern limb of the ACC, Fig. 5c). A weak Malvinas Current permits the Brazil Current to penetrate farther south, thereby displacing the BMC region southward and allowing the transport of warmer waters to higher latitudes. A region of warmer-than-normal SST is thus formed around 40°S in the western South Atlantic and spreads eastward along the Brazil–Malvinas extension (Fig. 6a).

Conversely, a decrease in SLP around 40°S (Fig. 4b) with the associated northward retreat of the subtropical anticyclone and the increase in intensity of the midlatitude westerlies (Fig. 4d), intensifies the subpolar gyre circulation (Fig. 5b) contributing to the strengthening of the Malvinas Current and the northward displacement of the BMC region (Fig. 5d). Cold waters are thus transported toward lower latitudes, which establishes a region of colder-than-normal SST along 40°S (Fig. 6b). Colder waters contribute to reduce the PPT along the SACZ and the ITCZ (Fig. 6d) and interact with the Tropics in such a way to enhance rainfall over northeast Brazil.

6. Conclusions

The MTM and SSA techniques provide independent evidence for the existence of a multidecadal cycle of the 25–30-yr period in the NCAR CCSM multicentury simulation for the South Atlantic region. To gauge the relevance of these oscillations in the real world, observational SST, SLP, and river flow data are also investigated and significant variability on those timescales is also found, despite the relative shortness of the records.

A cross correlation analysis suggests a strong relationship between the SSA-derived multidecadal components of the model SST, SLP, and PSI anomalies. Composites of model SST, SLP, PSI, wind stress, meridional current velocity, and precipitation anomalies are constructed based on the 25–30-yr period oscillatory component of SST. The dominant physical mechanism associated with this signal is the modulation of the ocean circulation on the northern limb of the ACC (i.e., the Malvinas and South Atlantic Currents) through changes in the southward extension of the subtropical anticyclone. A positive SLP anomaly along 40°S implies a southward extension of the subtropical anticyclone, thus a weakening of the climatological westerlies along 40°S and the associated reduction of the barotropic ocean transport. This implies a slowdown of the eastward-flowing South Atlantic Current and the northward-flowing Malvinas Current, which results in the displacement of the BMC region to the south. The warm Brazil Current waters are hence allowed to reach higher latitudes, which generates warmer-than-normal SST in the western subtropical South Atlantic. Such SST anomalies are then advected eastward by the South Atlantic Current along 40°S. Warm SST anomalies in the subtropics act to enhance PPT along the elongated SACZ and in the eastern Atlantic manifestation of the ITCZ. The reversed mechanisms are valid during the opposite phase of the multidecadal cycle. This sequence of processes is further supported by the lead–lag relationships derived from Fig. 3c. The change in the atmospheric circulation leads the change in the barotropic ocean transport by 1.5 years, and this in turn leads the change in the ocean temperatures by 1 year. This adds to a total of 2.5 years for the ocean temperatures to respond to the atmospheric circulation changes.

This scenario supports the idea of a basin-scale wind-driven oscillation causing modification of the oceanic mass transport and associated SST changes, with implications in the distribution of rainfall over South America. The rainfall anomalies are found to accompany the multidecadal variations of the SST index, in particular with respect to the model SACZ. In fact, a study of 40 years of National Centers for Environmental Prediction (NCEP)–NCAR reanalysis (Robertson and Mechoso 2000) suggests that the SST anomalies associated with the interannual changes of the SACZ could be related to variability in the BMC region.

Similar studies in the North Pacific and North Atlantic have proposed that atmospheric circulation changes lead to changes in the intensity of the subtropical gyre, which in turn modulate the strength of the poleward-flowing currents (Kuroshio and Gulf Stream, respectively) with a lag of several years. This variability is responsible for the subtropical SST anomaly in the western North Pacific and North Atlantic (Latif and Barnett 1994; Latif et al. 1996).

In our South Atlantic analysis, however, there is no evidence of intensity changes in the subtropical gyre due to atmospheric circulation changes. On the contrary, the changes in oceanic transport are focused in the region south of 35°S (Fig. 5), that is, the latitudes of the Malvinas Current and its extension to the east, the South Atlantic Current, on the northern limb of the ACC. We thus propose that the atmospheric circulation changes (SLP anomaly along 40°S) lead to changes in the equatorward-flowing Malvinas Current (and not in the poleward-flowing Brazil Current as in the Northern Hemisphere counterpart). The strength of the Malvinas Current controls the north–south position of the BMC region and thereby the southward penetration of the Brazil Current warm waters. As such, it is responsible for the SST anomaly in the western South Atlantic and in the Brazil–Malvinas eastward extension. This hypothesis would explain the relative short lag for the oceanic response to the wind stress forcing (1.5 years) compared to that found in the Northern Hemisphere studies. In the South Atlantic case, the changes in the wind stress affect directly the South Atlantic and Malvinas Currents transport, and the signal reaches the western boundary much faster than when it needs to propagate around the subtropical gyre.

These results would indicate that the timescale of the multidecadal oscillation is not set by the width of the South Atlantic basin (as in the North Pacific and North Atlantic cases). As such, the South Atlantic multidecadal variability could possibly be part of a hemisphere or global-scale mode, as was also suggested by Reason (2000). This issue remains to be further investigated.

In addition to the dynamic effect of the wind stress on the ocean transport, changes in the surface heat flux generated by the atmospheric circulation changes will tend to reinforce the initial subtropical SST anomalies. This heat flux mechanism was shown to be more important than the dynamic effect of the winds in the generation of subtropical SST anomalies in the Southern Hemisphere oceans (Reason 2000). Consistent with Reason (2000), it can be seen in Fig. 7 that the CCSM surface heat flux anomaly composites (corresponding to the positive and negative phases of the SST RC index) have the same sign as the SST anomaly composites (Figs. 6a,b) with the exception of the western boundary currents region, off the South American continent.

The presented analyses suggest that subtropical SST anomalies are generated through atmospherically driven changes in the intensity of the western boundary Malvinas Current. Further analyses would be required to investigate how or if these subtropical SST anomalies feed back on the atmospheric circulation, leading to its weakening or intensification. Based only on the results presented here, multidecadal variability in the South Atlantic appears to be predominantly driven by the atmosphere rather than coupled.

Acknowledgments

This work was supported in part by Grants FAPESP-98/13397-400/02958-5 and CNPq-300223/93-7. Additional travel support was provided to I.W. by the Climate and Global Dynamics Division at NCAR. NCAR is sponsored by the National Science Foundation. I.W. thanks Peter Gent, R. Saravanan, and J. Tribbia for usefull discussions, and the FERRET support group and Andy Roberston for the SSA-MTM toolkit reference. I.W. would also like to thank Norberto Garcia for the Parana River data. S.A.V. is grateful to the Danish National Research Foundation for its support of this work. Constructive comments and suggestions from two anonymous reviewers are gratefully acknowledged.

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Fig. 1.
Fig. 1.

Time series of anomalies: (a) observed SST (145 yr), (b) model SST (200 yr), (c) observed SLP (92 yr), (d) model SLP (200 yr), (e) observed Parana River flow (96 yr), (f) model PSI (200 yr). Time series are smoothed with a 5-yr running mean and standarized

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

Fig. 2.
Fig. 2.

MTM spectra (solid line) and the 90%, 95%, and 99% significance levels (dashed, dot–dashed, dotted lines, respectively) for the (a) observed SST, (b) model SST, (c) observed SLP, (d) model SLP, (e) Parana River flow, and (f) model PSI

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

Fig. 3.
Fig. 3.

Standardized reconstructed component (period about 25–28 yr, thick black line) derived using SSA with a 40-yr window of the model time series shown in Fig. 1, overlaid by model time series (thin black line) smoothed with a 5-yr running-mean filter for (a) SST, (b) SLP, and (c) PSI. (d) The lagged correlation between the reconstructed components SST and SLP (solid line), SST and PSI (dashed line), and PSI and SLP (dot–dashed line). The first quantity leads the second for positive lags

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

Fig. 4.
Fig. 4.

(top) SLP composites corresponding to the (a) positive and (b) negative phase of the SST RC index. (bottom) TAU composites corresponding to the (c) positive and (d) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

Fig. 5.
Fig. 5.

(top) PSI composites corresponding to the (a) positive and (b) negative phase of the SST RC index. (bottom) Meridional component of the ocean current field (V) composites corresponding to the (c) positive and (d) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

Fig. 6.
Fig. 6.

(top) SST composites corresponding to the (a) positive and (b) negative phase of the SST RC index. (bottom) PPT composites corresponding to the (c) positive and (d) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

Fig. 7.
Fig. 7.

Surface heat flux (°C cm s–1 × 105) composites corresponding to the (a) positive and (b) negative phase of the SST RC index. Extreme phases were obtained when the SST RC index exceeded ±1 std dev

Citation: Journal of Climate 15, 12; 10.1175/1520-0442(2002)015<1408:SAMVIT>2.0.CO;2

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